Posted by Matthias Grundmann, Research Scientist and Jianing Wei, Software Engineer, Google Research One of the most compelling things about smartphones today is the ability to capture a moment on the fly. With motion photos, a new camera feature available on the Pixel 2 and Pixel 2 XL phones, you no longer have to choose between a photo and a video so every photo you take captures more of the moment. When you take a photo with motion enabled, your phone also records and trims up to 3 seconds of video. Using advanced stabilization built upon technology we pioneered in Motion Stills for Android, these pictures come to life in Google Photos. Let’s take a look behind the technology that makes this possible!
Motion photos on the Pixel 2 in Google Photos. With the camera frozen in place the focus is put directly on the subjects. For more examples, check out this Google Photos album.
Camera Motion Estimation by Combining Hardware and Software The image and video pair that is captured every time you hit the shutter button is a full resolution JPEG with an embedded 3 second video clip. On the Pixel 2, the video portion also contains motion metadata that is derived from the gyroscope and optical image stabilization (OIS) sensors to aid the trimming and stabilization of the motion photo. By combining software based visual tracking with the motion metadata from the hardware sensors, we built a new hybrid motion estimation for motion photos on the Pixel 2.
Our approach aligns the background more precisely than the technique used in Motion Stills or the purely hardware sensor based approach. Based on Fused Video Stabilization technology, it reduces the artifacts from the visual analysis due to a complex scene with many depth layers or when a foreground object occupies a large portion of the field of view. It also improves the hardware sensor based approach by refining the motion estimation to be more accurate, especially at close distances.
Motion photo as captured (left) and after freezing the camera by combining hardware and software For more comparisons, check out this Google Photos album.
The purely software-based technique we introduced in Motion Stills uses the visual data from the video frames, detecting and tracking features over consecutive frames yielding motion vectors. It then classifies the motion vectors into foreground and background using motion models such as an affine transformation or a homography. However, this classification is not perfect and can be misled, e.g. by a complex scene or dominant foreground.
Feature classification into background (green) and foreground (orange) by using the motion metadata from the hardware sensors of the Pixel 2. Notice how the new approach not only labels the skateboarder accurately as foreground but also the half-pipe that is at roughly the same depth.
For motion photos on Pixel 2 we improved this classification by using the motion metadata derived from the gyroscope and the OIS. This accurately captures the camera motion with respect to the scene at infinity, which one can think of as the background in the distance. However, for pictures taken at closer range, parallax is introduced for scene elements at different depth layers, which is not accounted for by the gyroscope and OIS. Specifically, we mark motion vectors that deviate too much from the motion metadata as foreground. This results in a significantly more accurate classification of foreground and background, which also enables us to use a more complex motion model known as mixture homographies that can account for rolling shutter and undo the distortions it causes.
Background motion estimation in motion photos. By using the motion metadata from Gyro and OIS we are able to accurately classify features from the visual analysis into foreground and background.
Motion Photo Stabilization and Playback Once we have accurately estimated the background motion for the video, we determine an optimally stable camera path to align the background using linear programming techniques outlined in our earlier posts. Further, we automatically trim the video to remove any accidental motion caused by putting the phone away. All of this processing happens on your phone and produces a small amount of metadata per frame that is used to render the stabilized video in real-time using a GPU shader when you tap the Motion button in Google Photos. In addition, we play the video starting at the exact timestamp as the HDR+ photo, producing a seamless transition from still image to video.
Motion photos stabilize even complex scenes with large foreground motions.
Motion Photo Sharing Using Google Photos, you can share motion photos with your friends and as videos and GIFs, watch them on the web, or view them on any phone. This is another example of combining hardware, software and Machine Learning to create new features for Pixel 2.
Acknowledgements Motion photos is a result of a collaboration across several Google Research teams, Google Pixel and Google Photos. We especially want to acknowledge the work of Karthik Raveendran, Suril Shah, Marius Renn, Alex Hong, Radford Juang, Fares Alhassen, Emily Chang, Isaac Reynolds, and Dave Loxton.
Today, we are excited to announce the open source release of our latest and best performing semantic image segmentation model, DeepLab-v3+ , implemented in Tensorflow. This release includes DeepLab-v3+ models built on top of a powerful convolutional neural network (CNN) backbone architecture [2, 3] for the most accurate results, intended for server-side deployment. As part of this release, we are additionally sharing our Tensorflow model training and evaluation code, as well as models already pre-trained on the Pascal VOC 2012 and Cityscapes benchmark semantic segmentation tasks.
Since the first incarnation of our DeepLab model  three years ago, improved CNN feature extractors, better object scale modeling, careful assimilation of contextual information, improved training procedures, and increasingly powerful hardware and software have led to improvements with DeepLab-v2  and DeepLab-v3 . With DeepLab-v3+, we extend DeepLab-v3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further apply the depthwise separable convolution to both atrous spatial pyramid pooling [5, 6] and decoder modules, resulting in a faster and stronger encoder-decoder network for semantic segmentation.
Modern semantic image segmentation systems built on top of convolutional neural networks (CNNs) have reached accuracy levels that were hard to imagine even five years ago, thanks to advances in methods, hardware, and datasets. We hope that publicly sharing our system with the community will make it easier for other groups in academia and industry to reproduce and further improve upon state-of-art systems, train models on new datasets, and envision new applications for this technology.
Acknowledgements We would like to thank the support and valuable discussions with Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille (co-authors of DeepLab-v1 and -v2), as well as Mark Sandler, Andrew Howard, Menglong Zhu, Chen Sun, Derek Chow, Andre Araujo, Haozhi Qi, Jifeng Dai, and the Google Mobile Vision team.
Posted by Yang Song, Staff Software Engineer and Serge Belongie, Visiting Faculty, Google Research
Thanks to recent advances in deep learning, the visual recognition abilities of machines have improved dramatically, permitting the practical application of computer vision to tasks ranging from pedestrian detection for self-driving cars to expression recognition in virtual reality. One area that remains challenging for computers, however, is fine-grained and instance-level recognition. Earlier this month, we posted an instance-level landmark recognition challenge for identifying individual landmarks. Here we focus on fine-grained visual recognition, which is to distinguish species of animals and plants, car and motorcycle models, architectural styles, etc. For computers, discriminating fine-grained categories is challenging because many categories have relatively few training examples (i.e., the long tail problem), the examples that do exist often lack authoritative training labels, and there is variability in illumination, viewing angle and object occlusion.
To help confront these hurdles, we are excited to announce the 2018 iNaturalist Challenge (iNat-2018), a species classification competition offered in partnership with iNaturalist and Visipedia (short for Visual Encyclopedia), a project for which Caltech and Cornell Tech received a Google Focused Research Award. This is a flagship challenge for the 5th International Workshop on Fine Grained Visual Categorization (FGVC5) at CVPR 2018. Building upon the first iNaturalist challenge, iNat-2017, iNat-2018 spans over 8000 categories of plants, animals, and fungi, with a total of more than 450,000 training images. We invite participants to enter the competition on Kaggle, with final submissions due in early June. Training data, annotations, and links to pretrained models can be found on our GitHub repo.
iNaturalist has emerged as a world leader for citizen scientists to share observations of species and connect with nature since its founding in 2008. It hosts research-grade photos and annotations submitted by a thriving, engaged community of users. Consider the following photo from iNaturalist:
The map on the right shows where the photo was taken. Image credit: Serge Belongie.
You may notice that the photo on the left contains a turtle. But did you also know this is a Trachemys scripta, common name “Pond Slider?” If you knew the latter, you possess knowledge of fine-grained or subordinate categories.
In contrast to other image classification datasets such as ImageNet, the dataset in the iNaturalist challenge exhibits a long-tailed distribution, with many species having relatively few images. It is important to enable machine learning models to handle categories in the long-tail, as the natural world is heavily imbalanced – some species are more abundant and easier to photograph than others. The iNaturalist challenge will encourage progress because the training distribution of iNat-2018 has an even longer tail than iNat-2017.
Distribution of training images per species for iNat-2017 and iNat-2018, plotted on a log-log scale, illustrating the long-tail behavior typical of fine-grained classification problems. Image Credit: Grant Van Horn and Oisin Mac Aodha.
Along with iNat-2018, FGVC5 will also host the iMaterialist 2018 challenge (including a furniture categorization challenge and a fashion attributes challenge for product images) and a set of “FGVCx” challenges representing smaller scale – but still significant – challenges, featuring content such as food and modern art.
FGVC5 will be showcased on the main stage at CVPR 2018, thereby ensuring broad exposure for the top performing teams. This project will advance the state-of-the-art in automatic image classification for real world, fine-grained categories, with heavy class imbalances, and large numbers of classes. We cordially invite you to participate in these competitions and help move the field forward!
Acknowledgements We’d like to thank our colleagues and friends at iNaturalist, Visipedia, and FGVC5 for working together to advance this important area. At Google we would like to thank Hartwig Adam, Weijun Wang, Nathan Frey, Andrew Howard, Alessandro Fin, Yuning Chai, Xiao Zhang, Jack Sim, Yuan Li, Grant Van Horn, Yin Cui, Chen Sun, Yanan Qian, Grace Vesom, Tanya Birch, Wendy Kan, and Maggie Demkin.
Posted by André Araujo and Tobias Weyand, Software Engineers, Google Research
Image classification technology has shown remarkable improvement over the past few years, exemplified in part by the Imagenet classification challenge, where error rates continue to drop substantially every year. In order to continue advancing the state of the art in computer vision, many researchers are now putting more focus on fine-grained and instance-level recognition problems – instead of recognizing general entities such as buildings, mountains and (of course) cats, many are designing machine learning algorithms capable of identifying the Eiffel Tower, Mount Fuji or Persian cats. However, a significant obstacle for research in this area has been the lack of large annotated datasets.
Today, we are excited to advance instance-level recognition by releasing Google-Landmarks, the largest worldwide dataset for recognition of human-made and natural landmarks. Google-Landmarks is being released as part of the Landmark Recognition and Landmark Retrieval Kaggle challenges, which will be the focus of the CVPR’18 Landmarks workshop. The dataset contains more than 2 million images depicting 30 thousand unique landmarks from across the world (their geographic distribution is presented below), a number of classes that is ~30x larger than what is available in commonly used datasets. Additionally, to spur research in this field, we are open-sourcing Deep Local Features (DELF), an attentive local feature descriptor that we believe is especially suited for this kind of task.
Geographic distribution of landmarks in our dataset.
Landmark recognition presents some noteworthy differences from other problems. For example, even within a large annotated dataset, there might not be much training data available for some of the less popular landmarks. Additionally, since landmarks are generally rigid objects which do not move, the intra-class variation is very small (in other words, a landmark’s appearance does not change that much across different images of it). As a result, variations only arise due to image capture conditions, such as occlusions, different viewpoints, weather and illumination, making this distinct from other image recognition datasets where images of a particular class (such as a dog) can vary much more. These characteristics are also shared with other instance-level recognition problems, such as artwork recognition — so we hope the new dataset can benefit research for other image recognition problems as well.
The two Kaggle challenges provide access to annotated data to help researchers address these problems. The recognition track challenge is to build models that recognize the correct landmark in a dataset of challenging test images, while the retrieval track challenges participants to retrieve images containing the same landmark.
If you plan to be at CVPR this year, we hope you’ll attend the CVPR’18 Landmarks workshop. However, everyone is able to participate in the challenge, and access to the new dataset is available via the Kaggle website. We hope this resource is valuable to your research and we can’t wait to see the ideas you will come up with for recognizing landmarks!
Acknowledgments Jack Sim, Will Cukierski, Maggie Demkin, Hartwig Adam, Bohyung Han, Shih-Fu Chang, Ondrej Chum, Torsten Sattler, Giorgos Tolias, Xu Zhang, Fernando Brucher, Marco Andreetto, Gursheesh Kour.
Today, we are excited to announce the new Augmented Reality (AR) mode in Motion Stills for Android. With the new AR mode, a user simply touches the viewfinder to place fun, virtual 3D objects on static or moving horizontal surfaces (e.g. tables, floors, or hands), allowing them to seamlessly interact with a dynamic real-world environment. You can also record and share the clips as GIFs and videos.
Motion Stills with instant motion tracking in action
AR mode is powered by instant motion tracking, a six degree of freedom tracking system built upon the technology that powers Motion Text in Motion Stills iOS and the privacy blur on YouTube to accurately track static and moving objects. We refined and enhanced this technology to enable fun AR experiences that can run on any Android device with a gyroscope.
When you touch the viewfinder, Motion Stills AR “sticks” a 3D virtual object to that location, making it look as if it’s part of the real-world scene. By assuming that the tracked surface is parallel to the ground plane, and using the device’s accelerometer sensor to provide the initial orientation of the phone with respect to the ground plane, one can track the six degrees of freedom of the camera (3 for translation and 3 for rotation). This allows us to accurately transform and render the virtual object within the scene.
When the phone is approximately steady, the accelerometer sensor provides the acceleration due to the Earth’s gravity. For horizontal planes the gravity vector is parallel to normal of the tracked plane and can accurately provide the initial orientation of phone.
Instant Motion Tracking The core idea behind instant motion tracking is to decouple the camera’s translation and rotation estimation, treating them instead as independent optimization problems. First, we determine the 3D camera translation solely from the visual signal of the camera. To do this, we observe the target region's apparent 2D translation and relative scale across frames. A simple pinhole camera model relates both translation and scale of a box in the image plane with the final 3D translation of the camera.
The translation and the change in size (relative scale) of the box in the image plane can be used to determine 3D translation between two camera position C1 and C2. However, as our camera model doesn’t assume the focal length of the camera lens, we do not know the true distance/depth of the tracked plane.
To account for this, we added scale estimation to our existing tracker (the one used in Motion Text) as well as region tracking outside the field of view of the camera. When the camera gets closer to the tracked surface, the virtual content scales accurately, which is consistent with perception of real-world objects. When you pan outside the field of view of the target region and back the virtual object will reappear in approximately the same spot.
Independent translation (from visual signal only as shown by red box) and rotation tracking (from gyro; not shown)
After all this, we obtain the device’s 3D rotation (roll, pitch and yaw) using the phone’s built-in gyroscope. The estimated 3D translation combined with the 3D rotation provides us with the ability to render the virtual content correctly in the viewfinder. And because we treat rotation and translation separately, our instant motion tracking approach is calibration free and works on any Android device with a gyroscope.
Augmented chicken family with Motion Stills AR mode
We are excited to bring this new mode to Motion Stills for Android, and we hope you’ll enjoy it. Please download the new release of Motion Stills and keep sending us feedback with #motionstills on your favorite social media.
Acknowledgements For rendering, we are thankful we were able to leverage Google’s Lullaby engine using animated Poly models. A thank you to our team members who worked on the tech and this launch with us: John Nack, Suril Shah, Igor Kibalchich, Siarhei Kazakou, and Matthias Grundmann.
Posted by Michele Covell, Research Scientist, Google Research
Image compression is critical to digital photography — without it, a 12 megapixel image would take 36 megabytes of storage, making most websites prohibitively large. While the signal-processing community has significantly improved image compression beyond JPEG (which was introduced in the 1980’s) with modern image codecs (e.g., BPG, WebP), many of the techniques used in these modern codecs still use the same family of pixel transforms as are used in JPEG. Multiple recent Google projects improve the field of image compression with end-to-end with machine learning, compression through superresolution and creating perceptually improved JPEG images, but we believe that even greater improvements to image compression can be obtained by bringing this research challenge to the attention of the larger machine learning community.
To encourage progress in this field, Google, in collaboration with ETH and Twitter, is sponsoring the Workshop and Challenge on Learned Image Compression (CLIC) at the upcoming 2018 Computer Vision and Pattern Recognition conference (CVPR 2018). The workshop will bring together established contributors to traditional image compression with early contributors to the emerging field of learning-based image compression systems. Our invited speakers include image and video compression experts Jim Bankoski (Google) and Jens Ohm (RWTH Aachen University), as well as computer vision and machine learning experts with experience in video and image compression, Oren Rippel (WaveOne) and Ramin Zabih (Google, on leave from Cornell).
Training set of 1,633 uncompressed images from both the Mobile and Professional datasets, available on compression.cc
A database of copyright-free, high-quality images will be made available both for this challenge and in an effort to accelerate research in this area: Dataset P (“professional”) and Dataset M (“mobile”). The datasets are collected to be representative for images commonly used in the wild, containing thousands of images. While the challenge will allow participants to train neural networks or other methods on any amount of data (but we expect participants to have access to additional data, such as ImageNet and the Open Images Dataset), it should be possible to train on the datasets provided.
The first large-image compression systems using neural networks were published in 2016 [Toderici2016, Ballé2016] and were only just matching JPEG performance. More recent systems have made rapid advances, to the point that they match or exceed the performance of modern industry-standard image compression [Ballé2017, Theis2017, Agustsson2017, Santurkar2017, Rippel2017]. This rapid advance in the quality of neural-network-based compression systems, based on the work of a comparatively small number of research labs, leads us to expect even more impressive results when the area is explored by a larger portion of the machine-learning community.
We hope to get your help advancing the state-of-the-art in this important application area, and we encourage you to participate if you are planning to attend CVPR this year! Please see compression.cc for more details about the new datasets and important workshop deadlines. Training data is already available on that site. The test set will be released on February 15 and the deadline for submitting the compressed versions of the test set is February 22.
Posted by Hossein Talebi, Software Engineer and Peyman Milanfar Research Scientist, Machine Perception
Quantification of image quality and aesthetics has been a long-standing problem in image processing and computer vision. While technical quality assessment deals with measuring pixel-level degradations such as noise, blur, compression artifacts, etc., aesthetic assessment captures semantic level characteristics associated with emotions and beauty in images. Recently, deep convolutional neural networks (CNNs) trained with human-labelled data have been used to address the subjective nature of image quality for specific classes of images, such as landscapes. However, these approaches can be limited in their scope, as they typically categorize images to two classes of low and high quality. Our proposed method predicts the distribution of ratings. This leads to a more accurate quality prediction with higher correlation to the ground truth ratings, and is applicable to general images.
In “NIMA: Neural Image Assessment” we introduce a deep CNN that is trained to predict which images a typical user would rate as looking good (technically) or attractive (aesthetically). NIMA relies on the success of state-of-the-art deep object recognition networks, building on their ability to understand general categories of objects despite many variations. Our proposed network can be used to not only score images reliably and with high correlation to human perception, but also it is useful for a variety of labor intensive and subjective tasks such as intelligent photo editing, optimizing visual quality for increased user engagement, or minimizing perceived visual errors in an imaging pipeline.
Background In general, image quality assessment can be categorized into full-reference and no-reference approaches. If a reference “ideal” image is available, image quality metrics such as PSNR, SSIM, etc. have been developed. When a reference image is not available, “blind” (or no-reference) approaches rely on statistical models to predict image quality. The main goal of both approaches is to predict a quality score that correlates well with human perception. In a deep CNN approach to image quality assessment, weights are initialized by training on object classification related datasets (e.g. ImageNet), and then fine-tuned on annotated data for perceptual quality assessment tasks.
NIMA Typical aesthetic prediction methods categorize images as low/high quality. This is despite the fact that each image in the training data is associated to a histogram of human ratings, rather than a single binary score. A histogram of ratings is an indicator of overall quality of an image, as well as agreements among raters. In our approach, instead of classifying images a low/high score or regressing to the mean score, the NIMA model produces a distribution of ratings for any given image — on a scale of 1 to 10, NIMA assigns likelihoods to each of the possible scores. This is more directly in line with how training data is typically captured, and it turns out to be a better predictor of human preferences when measured against other approaches (more details are available in our paper).
Various functions of the NIMA vector score (such as the mean) can then be used to rank photos aesthetically. Some test photos from the large-scale database for Aesthetic Visual Analysis (AVA) dataset, as ranked by NIMA, are shown below. Each AVA photo is scored by an average of 200 people in response to photography contests. After training, the aesthetic ranking of these photos by NIMA closely matches the mean scores given by human raters. We find that NIMA performs equally well on other datasets, with predicted quality scores close to human ratings.
Ranking some examples labelled with the “landscape” tag from AVA dataset using NIMA. Predicted NIMA (and ground truth) scores are shown below each image.
NIMA scores can also be used to compare the quality of images of the same subject which may have been distorted in various ways. Images shown in the following example are part of the TID2013 test set, which contain various types and levels of distortions.
Ranking some examples from TID2013 dataset using NIMA. Predicted NIMA scores are shown below each image.
Perceptual Image Enhancement As we’ve shown in another recent paper, quality and aesthetic scores can also be used to perceptually tune image enhancement operators. In other words, maximizing NIMA score as part of a loss function can increase the likelihood of enhancing perceptual quality of an image. The following example shows that NIMA can be used as a training loss to tune a tone enhancement algorithm. We observed that the baseline aesthetic ratings can be improved by contrast adjustments directed by the NIMA score. Consequently, our model is able to guide a deep CNN filter to find aesthetically near-optimal settings of its parameters, such as brightness, highlights and shadows.
NIMA can be used as a training loss to enhance images. In this example, local tone and contrast of images is enhanced by training a deep CNN with NIMA as its loss. Test images are obtained from the MIT-Adobe FiveK dataset.
Looking Ahead Our work on NIMA suggests that quality assessment models based on machine learning may be capable of a wide range of useful functions. For instance, we may enable users to easily find the best pictures among many; or to even enable improved picture-taking with real-time feedback to the user. On the post-processing side, these models may be used to guide enhancement operators to produce perceptually superior results. In a direct sense, the NIMA network (and others like it) can act as reasonable, though imperfect, proxies for human taste in photos and possibly videos. We’re excited to share these results, though we know that the quest to do better in understanding what quality and aesthetics mean is an ongoing challenge — one that will involve continuing retraining and testing of our models.
Since we released AIY Voice Kit, we've been inspired by the thousands of amazing builds coming in from the maker community. Today, the AIY Team is excited to announce our next project: the AIY Vision Kit — an affordable, hackable, intelligent camera.
Much like the Voice Kit, our Vision Kit is easy to assemble and connects to a Raspberry Pi computer. Based on user feedback, this new kit is designed to work with the smaller Raspberry Pi Zero W computer and runs its vision algorithms on-device so there's no cloud connection required.
Build intelligent devices that can perceive, not just see
The kit materials list includes a VisionBonnet, a cardboard outer shell, an RGB arcade-style button, a piezo speaker, a macro/wide lens kit, flex cables, standoffs, a tripod mounting nut and connecting components.
The VisionBonnet is an accessory board for Raspberry Pi Zero W that features the Intel® Movidius™ MA2450, a low-power vision processing unit capable of running neural networks. This will give makers visual perception instead of image sensing. It can run at speeds of up to 30 frames per second, providing near real-time performance.
Bundled with the software image are three neural network models:
A model based on MobileNetsthat can recognize a thousand common objects.
A model for face detection capable of not only detecting faces in the image, but also scoring facial expressions on a "joy scale" that ranges from "sad" to "laughing."
A model for the important task of discerning between cats, dogs and people.
For those of you who have your own models in mind, we've included the original TensorFlow code and a compiler. Take a new model you have (or train) and run it on the the Intel® Movidius™ MA2450.
Extend the kit to solve your real-world problems
The AIY Vision Kit is completely hackable:
Want to prototype your own product? The Vision Kit and the Raspberry Pi Zero W can fit into any number of tiny enclosures.
Want to change the way the camera reacts? Use the Python API to write new software to customize the RGB button colors, piezo element sounds and GPIO pins.
Want to add more lights, buttons, or servos? Use the 4 GPIO expansion pins to connect your own hardware.
We hope you'll use it to solve interesting challenges, such as:
Build "hotdog/not hotdog" (or any other food recognizer)
Turn music on when someone walks through the door
Send a text when your car leaves the driveway
Open the dog door when she wants to get back in the house
*** Please note that AIY Vision Kit requires Raspberry Pi Zero W, Raspberry Pi Camera V2 and a micro SD card, which must be purchased separately.
Tell us what you think!
We're listening — let us know how we can improve our kits and share what you're making using the #AIYProjects hashtag on social media. We hope AIY Vision Kit inspires you to build all kinds of creative devices.
Looking at a landmark and not sure what it is? Interested in learning more about a movie as you stroll by the poster? With Google Lens and your Google Assistant, you now have a helpful sidekick to tell you more about what’s around you, right on your Pixel.
When we introduced the new Pixel 2 last month, we talked about how Google Lens builds on Google’s advancements in computer vision and machine learning. When you combine that with the Google Assistant, which is built on many of the same technologies, you can get quick help with what you see. That means that you can learn more about what’s in front of you—in real time—by selecting the Google Lens icon and tapping on what you’re interested in.
Here are the key ways your Assistant and Google Lens can help you today:
Text: Save information from business cards, follow URLs, call phone numbers and navigate to addresses.
Landmarks: Explore a new city like a pro with your Assistant to help you recognize landmarks and learn about their history.
Art, books and movies: Learn more about a movie, from the trailer to reviews, right from the poster. Look up a book to see the rating and a short synopsis. Become a museum guru by quickly looking up an artist’s info and more. You can even add events, like the movie release date or gallery opening, to your calendar right from Google Lens.
Barcodes: Quickly look up products by barcode, or scan QR codes, all with your Assistant.
Google Lens in the Assistant will be rolling out to all Pixel phones set to English in the U.S., U.K., Australia, Canada, India and Singapore over the coming weeks. Once you get the update, go to your Google Assistant on your phone and tap the Google Lens icon in the bottom right corner.
We can’t wait to see how Google Lens helps you explore the world around you, with the help of your Google Assistant. And don’t forget, Google Lens is also available in Google Photos, so even after you take a picture, you can continue to explore and get more information about what’s in your photo.
Posted by Ibrahim Badr, Associate Product Manager, Google Assistant
Posted by Chia-Kai Liang, Senior Staff Software Engineer and Fuhao Shi, Android Camera Team
One of the most important aspects of current smartphones is easily capturing and sharing videos. With the Pixel 2 and Pixel 2 XL smartphones, the videos you capture are smoother and clearer than ever before, thanks to our Fused Video Stabilization technique based on both optical image stabilization (OIS) and electronic image stabilization (EIS). Fused Video Stabilization delivers highly stable footage with minimal artifacts, and the Pixel 2 is currently rated as the leader in DxO's video ranking (also earning the highest overall rating for a smartphone camera). But how does it work?
A key principle in videography is keeping the camera motion smooth and steady. A stable video is free of the distraction, so the viewer can focus on the subject of interest. But, videos taken with smartphones are subject to many conditions that make taking a high-quality video a significant challenge:
Camera Shake Most people hold their mobile phones in their hands to record videos - you pull the phone from your pocket, record the video, and the video is ready to share right after recording. However, that means your videos shake as much as your hands do -- and they shake a lot! Moreover, if you are walking or running while recording, the camera motion can make videos almost unwatchable: Motion Blur If the camera or the subject moves during exposure, the resulting photo or video will appear blurry. Even if we stabilize the motion in between consecutive frames, the motion blur in each individual frame cannot be easily restored in practice, especially on a mobile device. One typical video artifact due to motion blur is sharpness inconsistency: the video may rapidly alternate between blurry and sharp, which is very distracting even after the video is stabilized: Rolling Shutter The CMOS image sensor collects one row of pixels, or “scanline”, at a time, and it takes tens of milliseconds to goes from the top scanline to the bottom. Therefore, anything moving during this period can appear distorted. This is called the rolling shutter distortion. Even if you have a steady hand, the rolling shutter distortion will appear when you move quickly:
A simulated rendering of a video with global (left) and rolling (right) shutter.
Focus Breathing When there are objects of varying distance in a video, the angle of view can change significantly due to objects “jumping” in and out of the foreground. As result, everything shrinks or expands like the video below, which professionals call “breathing”: A good stabilization system should address all of these issues: the video should look sharp, the motion should be smooth, and the rolling shutter and focus breathing should be corrected.
Many professionals mount the camera on a mechanical stabilizer to entirely isolate hand motion. These devices actively sense and compensate for the camera’s movement to remove all unwanted motions. However, they are usually expensive and cumbersome; you wouldn’t want to carry one every day. There are also handheld gimbal mounts available for mobile phones. However, they are usually larger than the phone itself, and you have to put the phone on it before start recording. You’d need to do it fast before the interesting moment vanishes.
Optical Image Stabilization (OIS) is the most well-known method for suppression of handshake artifacts. Typically, in mobile camera modules with OIS, the lens is suspended in the middle of the module by a number of springs and electromagnets are used to move the lens within its enclosure. The lens module actively senses and compensates for handshake motion at very high speeds. Because OIS responds to motion rapidly, it can greatly suppress the handshake blur. However, the range of correctable motion is fairly limited (usually around 1-2 degrees), which is not enough to correct the unwanted motions between consecutive video frames, or to correct excessive motion blur during walking. Moveover, OIS cannot correct some kinds of motions, such as in-plane rotation. Sometimes it can even introduce a “jello” artifact:
The video is taken by Pixel 2 with only OIS enabled. You can see the frame center is stabilized, but the boundaries have some jello-like artifacts.
Electronic Image Stabilization (EIS) analyzes the camera motion, filters out the unwanted parts, and synthesizes a new video by transforming each frame. The final stabilization quality depends on the algorithm design and implementation optimization of these stages. In general, software-based EIS is more flexible than OIS so it can correct larger and more kinds of motions. However, EIS has some common limitations. First, to prevent undefined regions in the synthesized frame, it needs to reduce the field of view or resolution. Second, compared to OIS or an external stabilizer, EIS requires more computation, which is a limited resource on mobile phones.
Making a Better Video: Fused Video Stabilization With Fused Video Stabilization, both OIS and EIS are enabled simultaneously during video recording to address all the issues mentioned above. Our solution has three processing stages as shown in the system diagram below. The first processing stage, motion analysis, extracts the gyroscope signal, the OIS motion, and other properties to estimate the camera motion precisely. Then, the motion filtering stage combines machine learning and signal processing to predict a person’s intention in moving the camera. Finally, in the frame synthesis stage, we model and remove the rolling shutter and focus breathing distortion. With Fused Video Stabilization, the videos from Pixel 2 have less motion blur and look more natural. The solution is efficient enough to run in all video modes, such as 60fps or 4K recording.
Motion Analysis In the motion analysis stage, we use the phone’s high-speed gyroscope to estimate the rotational component of the hand motion (roll, pitch, and yaw). By sensing the motion at 200 Hz, we have dense motion vectors for each scanline, enough to model the rolling shutter distortion. We also measure lens motions that are not sensed by the gyroscope, including both the focus adjustment (z) and the OIS movement (x and y) at high speed. Because we need high temporal precision to model the rolling shutter effect, we carefully optimize the system to ensure perfect timestamp alignment between the CMOS image sensor, the gyroscope, and the lens motion readouts. A misalignment of merely a few milliseconds can introduce noticeable jittering artifact:
Left: The stabilized video of a “running” motion with a 3ms timing error. Note the occasional jittering. Right: The stabilized video with correct timestamps. The bottom right corner shows the original shaky video.
Motion Filtering The motion filtering stage takes the real camera motion from motion analysis and creates the stabilized virtual camera motion. Note that we push the incoming frames into a queue to defer the processing. This enables us to lookahead at future camera motions, using machine learning to accurately predict the user’s intention. Lookahead filtering is not feasible for OIS or any mechanical stabilizers, which can only react to previous or present motions. We will discuss more about this below.
Frame Synthesis At the final stage, we derive how the frame is transformed based on the real and virtual camera motions. To handle the rolling shutter distortion, we use multiple transformations for each frame. We split the the input frame into a mesh and warp each part separately:
Left: The input video with mesh overlay. Right: The warped frame, and the red rectangle is the final stabilized output. Note how the non-rigid warping corrects the rolling shutter distortion.
Lookahead Motion Filtering One key feature in the Fused Video Stabilization is our new lookahead filtering algorithm. It analyzes future motions to recognize the user-intended motion patterns, and creates a smooth virtual camera motion. The lookahead filtering has multiple stages to incrementally improve the virtual camera motion for each frame. In the first step, a Gaussian filtering is applied on the real camera motions of both past and future to obtain a smoothed camera motion:
Left: The input unstabilized video. Right: The smoothed result after Gaussian filtering.
You’ll notice that it’s still not very stable. To further improve the quality, we trained a model to extract intentional motions from the noisy real camera motions. We then apply additional filters given the predicted motion. For example, if we predict the camera is panning horizontally, we would reject more vertical motions. The result is shown below.
Left: The Gaussian filtered result. Right: Our lookahead result. We predict that the user is panning to the right, and suppress more vertical motions.
In practice, the process above does not guarantee there is no undefined “bad” regions, which can appear when the virtual camera is too stabilized and the warped frame falls outside the original field of view. We predict the likelihood of this issue in the next couple frames and adjust the virtual camera motion to get the final result.
Left: Our lookahead result. The undefined area at the bottom-left are shown in cyan. Right: The final result with the bad region removed.
As we mentioned earlier, even with OIS enabled, sometimes the motions are too large and cause motion blur in a single frame. When EIS is further applied to further smooth the camera motion, the motion blur leads to distracting sharpness variations:
Left: Pixel 2 with OIS only. Right: Pixel 2 with the basic Fused Video Stabilization. Note that sharpness variation around the “Exit” label.
This is a very common problem in EIS solutions. To address this issue, we exploit the “masking” property in the human visual system. Motion blur usually blurs the frame along a specific direction, and if the overall frame motion follows that direction, the human eye will not notice it. Instead, our brain treats the blur as a natural part of the motion, and masks it away from our perception.
With the high-frequency gyroscope and OIS signals, we can accurately estimate the motion blur for each frame. We compute where the camera pointed to at both the beginning and end of exposure, and the movement in-between is the motion blur. After that, we apply a machine learning algorithm (trained on a set of videos with and without motion blur) to map the motion blurs in past and future frames to the amount of real camera motion we want to keep, and blend the weighted real camera motion with the virtual one. As you can see below, with the motion blur masking, the distracting sharpness variation is greatly reduced and the camera motion is still stabilized.
Left: Pixel 2 with the basic Fused Video Stabilization. Right: The full Fused Video Stabilization solution with motion blur masking.
Results We have seen many amazing videos from Pixel 2 with Fused Video Stabilization. Here are some for you to check out:
Videos taken by two Pixel 2 phones mounted on a single hand grip. Fused Video Stabilization is disabled in the left one.
Videos taken by two Pixel 2 phones mounting on a single hand grip. Fused Video Stabilization is disabled in the left one. Note that the videographer jumped together with the subject.
Fused Video Stabilization combines the best of OIS and EIS, shows great results in camera motion smoothing and motion blur reduction, and corrects both rolling shutter and focus breathing. With Fused Video Stabilization on the Pixel 2 and Pixel 2 XL, you no longer have to carefully place the phone before recording, hold it firmly over the entire recording session, or carry a gimbal mount everywhere. The recorded video will always be stable, sharp, and ready to share.
Acknowledgements Fused Video Stabilization is a large-scale effort across multiple teams in Google, including the camera algorithm team, sensor algorithm team, camera hardware team, and sensor hardware team.