We recently took a look at the new Amazon Fire TV box, and now we’re giving one away! Enter the competition using the widget below. The competition lasts for about two weeks and is open to all readers where the Fire TV is available. Good luck! Amazon Fire TV (2017)
Browsers are the windows to the world wide web. Google Chrome rules the roost today, but a few new challengers might fit your needs better. For all its great features, Chrome hogs system memory and drains the battery. But that huge collection of extensions, and exclusive features like Translate and Google Cast, make it hard to give up on. So in case you’re feeling trapped in Chrome, try out one of these. 1. Colibri (Windows, Mac): Distraction-Free Browser Without Tabs The introduction of tabs changed internet browsing back in the day. Colibri wants to go back to those old days....
Rumors in early October had Nintendo more than doubling production for the highly successful Switch in order to meet what will almost certainly be crushing demand during the holidays. The company may be planning even higher volumes for next year — perhaps as many as 30 million consoles. Read More
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Thanksgiving approaches faster than a turkey running away from the chopping block. Or that sense of foreboding that goes hand in hand with familial visits. With it comes Black Friday for unbelievable deals. Whether you’re shopping for yourself or holiday shopping for friends and family, these are the best Black Friday deals around. Note: Some of these deals won’t be available until Black Friday begins at 12am EST on November 24, 2017. LG 49-Inch 4K LED UHD Smart TV ($400 at Kohl’s) Image Credit: Kohl’s Kohl’s may not rank among the most well-known electronics retailers like Best Buy. Nevertheless, Kohl’s...
Trading cards on Steam tap into our love of collecting, while also providing a way to trade and make money on the platform’s marketplace. In fact, many gamers idle their eligible Steam games to collect these cards. While some just leave a game open on their computer and wait for time to pass, others take a more methodical approach with idling software. These programs idle your games without the need to open them at all. For many, it’s how you turn game achievements into money. Two major options, IdleMaster and ArchiSteamFarm, will allow you to earn trading cards on Steam without even opening (or...
Spin, one of several bike-sharing companies now vying for dominance in a number of U.S. cities, has a new weapon to wield against its competitors: Kyle Rowe, the architect of Seattle’s permissive and apparently successful bike-share permit system, who is joining the company to work on government partnerships. In addition, Spin is gearing up to launch in a dozen or so new cities. Read More
Uber’s found a way to shave another 25% or so off the price of a ride. Uber Express Pool asks you to walk up to a few blocks to your pickup and destination before and after the ride in exchange for the cheapest fare out of all of Uber’s options. The feature dynamically generates “smart spots” that are easy to drive to and close to the origin or destination of… Read More
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Instagram is testing a new feature with a small fraction of its user base. As spotted by Pippa Akram and The Next Web, some users can now search for a hashtag and follow this tag in particular. Ok this is new. What does this do @SimonSocialMM @BizPaul @NatalieTFG any ideas? I've followed 2 but can't find what that means!! http://pic.twitter.com/LlCBk4Wmfv — Pippa Akram… Read More Read Full Article
Logitech is intentionally bricking Harmony Link devices in March 2018. This means your Harmony Link will “no longer function after this date”. However, after the backlash that followed this announcement, Logitech is offering a Harmony Hub to all Harmony Link owners for free. All hardware manufacturers stop supporting their products eventually. It’s the only way they can operate, or they’ll be forced to support countless products forever. However, when it comes to cloud-based products, ending support can essentially stop these products from working… Logitech Responds to Complaints Earlier this week, it was revealed that Logitech is planning to brick all...
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 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:
The videos are taken by two Pixel 2 phones mounted on a single hand grip. Fused Video Stabilization is disabled in the left one.
The videos are 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 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.
It wouldn’t be the launch of a first-generation Apple product without a few hiccups — who can forget Antennagate and Bendgate? — and the iPhone X is no different. A handful of users report that a mysterious and inextinguishable green line has appeared on their device’s display. Read More
It wouldn’t be the launch of a first-generation Apple product without a few hiccups — who can forget Antennagate and Bendgate? — and the iPhone X is no different. A handful of users report that a mysterious and inextinguishable green line has appeared on their device’s display. Read More
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