In many cases, low bandwidth networks or data caps prevent us from delivering the perfect picture. To address this, the Netflix Video Algorithms team has been working on more efficient compression algorithms that enable Netflix to deliver the same or better picture quality while using less bandwidth. And working together with other engineering teams at Netflix, we update our client applications and streaming infrastructure to support the new video streams and to ensure seamless playback on Netflix devices.
Our next step was productizing a shot-based encoding framework, called Dynamic Optimizerresulting in more granular optimizations within a video stream. In this article we describe some of the implementation challenges we overcame in bringing this framework into our production pipeline, and practical results on how it improves video quality for our members. As described in more detail in this blog postthe Dynamic Optimizer analyzes an entire video over multiple quality and resolution points in order to obtain the optimal compression trajectory for an encode, given an optimization objective.
In particular, we utilize VMAFthe Netflix subjective video quality metric, as our optimization objective, since our goal is to generate streams at the best perceptual quality. The primary challenge we faced in implementing the Dynamic Optimizer framework in production was retrofitting our parallel encoding pipeline to process significantly more encode units. First, the analysis step for the Dynamic Optimizer required encoding with different resolutions and qualities QPsrequiring an order of magnitude more complexity.
Second, we transitioned from encoding video chunks of about a few minutes long, to video encodes on a per-shot basis. For example, in the original system, a 1-hour episode of Stranger Things results in twenty 3-minute chunks. With shot-based encoding, with an average shot-length of 4 seconds, the same episode requires processing of shots.
Assuming each chunk corresponds to a shot Fig. This increase exposed system bottlenecks related to the number of messages passed between compute instances. Several engineering innovations were performed to address the limitations and we discuss two of them here: Collation and Checkpoints. While we could have improved the core messaging system to handle such an increase in message volume, it was not the most feasible and expedient solution at that time.
We instead adapted our pipeline by introducing collation. In collation, we collate shots together, so that a set of consecutive shots make up a chunk. Now, given that we have flexibility on how such collation occurs, we can group an integer number of shots together so that we produce approximately the same 3-minute chunk duration that we produced initially, under the chunk-based encode model Fig. These chunks could be configured to be approximately the same size, which helps with resource allocation for instances previously tuned for encoding of chunks a few minutes long.
Within each chunk, the compute instance independently encodes each of the shots, with its own set of defined parameters. Collating independently encoded shots within a chunk led to an additional system improvement we call checkpoints. Previously, if we lost a compute instance because we had borrowed it and it was suddenly needed for higher priority taskswe re-encoded the entire chunk. In the case of shots, each shot is independently encoded.
Once a shot is completed, it does not need to be re-encoded if the instance is lost while encoding the rest of the chunk. We created a system of checkpoints Fig. Now, if the same chunk is retried on another compute instance, encoding does not start from scratch but from the shot where it left off, bringing computational savings. For these mobile encodes, several changes led to improved compression performance over per-title encodes, including longer GOPs, flexible encoder settings and per-chunk optimization.
These streams serve as our high quality baseline for H. The graph below Fig. To construct this graph, we took a sample of thousands of titles from our catalog. We then averaged VMAF values across all the titles for the given x, which provided one point for each curve in the following figure.
Sweeping over all bitrate values x, this resulted in 5 curves, corresponding to the 5 types of encodes discussed above. Assuming stable network conditions, this is the average VMAF quality you will receive on the Netflix service at that particular video bandwidth.
We can see that, compared to per-title encoding with AVCMain, the optimized encodes require less than half of the bits to achieve the same quality.
We also examine how visual quality is impacted given the same bandwidth. For example, an average cellular connection bandwidth of kbps, results in the average VMAF values shown in the table below. The optimized encodes provide noticeably better video quality than AVCMain.
Immediately noticeable is the increased quality in textures bricks, trees, rocks, water etc. The VP9-Opt frame Fig.Some feel that, with the higher coding efficiency of the H. I must admit that I have heard this compression is dead argument at least four times since I started working in image and video coding in People were postulating that video coding was dead more than four decades ago.
Is image and video coding dead? From the standpoint of application and relevance, video compression is very much alive and kicking and thriving on the internet. As for industry involvement in video coding researchit appears that the area is more active than ever before.
Is HDTV Compression Damaging Picture Quality?
The Alliance for Open Media AOM was founded in by leading tech companies to collaborate on an open and royalty-free video codec. The goal of AOM was to develop video coding technology that was efficient, cost-effective, high quality and interoperable, leading to the launch of AV1 this year.
The recently-concluded Call for Proposals attracted an impressive number of 32 institutions from industry and academia, with a combined 22 submissions. Like many global internet companies, Netflix realizes that advancements in video coding technology are crucial for delivering more engaging video experiences.
On one end, many people are constrained by unreliable networks or limited data plans, restricting the video quality that can be delivered with current technology. On the other side of the spectrum, premium video experiences like 4K UHD, degree video and VR, are extremely data-heavy.House of bulk south africa
Video compression gains are necessary to fuel the adoption of these immersive video technologies. So how will we get to deliver HD quality Stranger Things at kbps for the mobile user in rural Philippines? Radically new ideas. And forums like the Picture Coding Symposium where the video coding community can share, learn and introspect. Influenced by our product roles at Netflix, exposure to the standardization community and industry partnerships, and research collaboration with academic institutions, we share some of our questions and thoughts on the current state of video coding research.
These ideas have inspired us as we embarked on organizing the special sessions, keynote speeches and invited talks for PCS Attempts to veer away from this traditional model have been unsuccessful. In some cases say, distributed video codingit was because the technology was impractical for the prevalent use case. In most other cases, however, it is likely that not enough resources were invested in the new technology to allow for maturity.
Unfortunately, new techniques are evaluated against the state-of-the-art codec, for which the coding tools have been refined from decades of investment. How many redundant bits can we squeeze out if we simply stay on the paved path and iterate on the same set of encoding tools?
The community needs better ways to measure video quality. In academic publications, standardization activities, and industry codec evaluations, PSNR remains the gold standard for evaluating encoding performance. And yet every person in the field will tell you that PSNR does not accurately reflect human perception. Encoding tools like adaptive quantization and psycho-visual optimization claim to improve visual quality but fare worse in terms of PSNR.
So researchers and engineers augment the objective measurements with labor-intensive visual subjective tests. Although this evaluation methodology has worked for decades, it is infeasible for large scale evaluation, especially, if the test set spans diverse content and wide quality ranges.
For the video codec community to innovate more quickly, and more accurately, automated video quality measurements that better reflect human perception should be utilized. These new metrics have to be widely agreed upon and adopted, so it is necessary that they open and independently verifiable.
Can we confidently move video encoding technology without solving the problem of automated video quality assessment first? Encouraging new ideas means discussing with new people. I Anne attended my first MPEG meeting three years ago where I presented an input document on Netflix use cases for future video coding. I claimed that for the Netflix application, encoding complexity increase is not a concern if it comes with significant compression improvement.
We run compute on the cloud and have no real-time requirements. People typically say 3X. The video coding community today is composed of research groups in academia, institutions active in video standardization, companies implementing video codec technologies and technology and entertainment companies deploying video services.
How do we foster more cross-pollination and collaboration across these silos to positively lift all boats? The talks and panel discussion aim to connect PCS researchers with related fields and communities.Netflix offers 4K movies, but are they good enough to compete with the UHD Blu-ray you can find in a store? Well, no. Streaming will almost always look worse, but for some things it might not matter. Not exactly.
To dramatically oversimplify a complex topic, compression lowers the file size of a video by tossing out redundant or unnecessary information. Compression also reduces file sizes by reducing the quality of a picture, usually imperceptibly. An uncompressed 4K video would be over 5 terabytes of data per hour, which is way too much to put on a Blu-ray or even stream. Compressing the picture quality—by tossing out very minor pixel detail, for example—gets it down to a much more reasonable size.
So, Netflix compresses your movies and shows a lot more than a Blu-ray would. How much more? The result is watching a show on Netflix will sometimes look a lot worse than if you were to watch those same shows on a Blu-ray.
This is especially true of scenes with rain, snow, or confettiwhich trip up compression algorithms more than usual. Some scenes—especially scenes with little motion and simple images, like say a cartoon—will look perfectly fine. But scenes with a lot of detail or movement can end up looking pixelated or choppy because compressing the show down to a streamable size throws out a lot of data.
If you care about getting the best picture quality possible, then yes, the Blu-ray will almost always be the best version of the movie. The average home broadband speed is getting higherwhich might make higher quality 4K streaming video an option though video providers still need to stream at higher speeds. Or if you use an app like Plex to host your own movies, you can get a lot more control over the streaming quality. Is that extra expense worth it to get some finer pixel quality from your movies?
It might be! High-dynamic range or HDR is an even more important new technology that gives you brighter lights, darker blacks, and increases the range of colors your TV can display. Arguably, HDR is a much bigger innovation. HDR has an interesting relationship with bitrate. Sounds like it would be a lot more data to stream, right? However, compression changes the game here again.
Since HDR movies can display more nuanced colors, the footage can be compressed further while still leaving in the same amount of detail. In some cases, the bitrate increase can be as low as zero. HDR is capable of displaying over a billion. It gives you more bandwidth and a higher quality source for your favorite movies. Comments 0. The above article may contain affiliate links, which help support Review Geek.
Skip to content Home Office Kitchen Accessories. Smarthome Phones Chargers Gaming. Deals Buying Guides Reviews. The Best Newsletter Anywhere Joinsubscribers and get a daily digest of news, articles, and more. Eric Ravenscraft Eric Ravenscraft has nearly a decade of writing experience in the technology industry. Recently Popular Google recently made a change to how it displays and differentiates ads from organic results in search. With this change, ads become slightly more indistinguishable from other results, which has some people up in arms.
But I submit a question: does it really matter if it still takes you where you need to go? With the rise of smartphones as the primary digital interaction for most people, unscrupulous developers are finding more ingenious ways to scam people out of money.What happens when manufacturers of e.Fast buds united states
Series compress your videos too much to upload them to Netflix. But that's on your side because the download is too slow. In conventional compression processes, you can clearly notice so-called compression artifacts, which are structures in the image that only arise from compression and are not present in the raw material. They will soon become more and more.
Another possibility for compression is to lower the resolution, what should happen to the image material should be clear. The distributors of the series will hardly make the compression so strong on Netflix that the image material will suffer visibly.
At most, it may be that Netflix itself lowers the resolution if your internet connection is too slow. Ok thanks, it was just about the fact that on my P30 pro in videos the background is sometimes edgy no matter on Youtube or Netflix and I was told that it would be because of the video's commission.
Yes it does, but this is probably due to the fact that your internet connection is too slow or Netflix and Youtube think that your connection is too slow. So I have not seen this with HD films yet and I do not think that the producers themselves compress the film so strongly. The content producers do this to the required minimum quality or better quality.
Isn't that the Netflix that is not controlled, on the contrary, there are the meticulous pixel counters and the delivered resolution is measured to avoid that in the end only FHD material that is upscaled in the post is cheered. For example, Arri changed the sensor and the readout method for Netflix and converted old Alexa and Amira to "Netflix versions". The problem is the download or its bandwidth if you use stream because delivery quality or QoS Quality of Service is mostly used.
Movies Anime Free. Too much compression what are the consequences? Ja JadeNation. Ce Centvaughn. That does not happen.
Use Wi-Fi. Ha Hallie9. The quality of the footage just gets bad. Ta Tanker The reason was missing for Netflix by pixels with the required DCI4K minimum image width or otherwise UHD resolution is not sufficient for Netflix The problem is the download or its bandwidth if you use stream because delivery quality or QoS Quality of Service is mostly used.
Fake data for Netflix. What can this have for consequences? Qu QuicksandStaking. Are all consequences of SAO on Netflix?
Why is compression and macroblocking in streams not being discussed?
Netflix 4K Ultra HD Review
Thread starter Bhamnerky Start date Jan 11, Bhamnerky Novice Member. There are endless articles about 4K and now 8k. Got DirectTV?The Confession Killer - Official Trailer - Netflix
Forget it! Why are more people not complaining? How will you enjoy Game of Thrones in this coming season on your 4K tv? I hope they stay outside! What upsets me the most.
Netflix has proven to me that if CAN be done. Watch Bird Box in Dolby Vision. Looks great! What am I missing? What are others doing? Just watching blue rays all day? You have to ask Netflix why their streams are sent at a lower bit rate.
Is It Better to Watch a 4K Movie On Blu-ray or Through Streaming?
For audio, it is under 1 Mpbs most likely to ARC bandwidth limitations. Video, for normal HD it is around Mbps which is way too low. It is clear they are playing safe by catering to the majority under the assumption they don't have high speed broadband.
UHD, it is. All this can be mitigated by increasing the bit rate. The content owners own this problem and are in full control. My point of the post is to create a dialogue and get consumers properly educated.What happens when manufacturers of e. Series compress your videos too much to upload them to Netflix. But that's on your side because the download is too slow. In conventional compression processes, you can clearly notice so-called compression artifacts, which are structures in the image that only arise from compression and are not present in the raw material.
They will soon become more and more. Another possibility for compression is to lower the resolution, what should happen to the image material should be clear. The distributors of the series will hardly make the compression so strong on Netflix that the image material will suffer visibly. At most, it may be that Netflix itself lowers the resolution if your internet connection is too slow.
Ok thanks, it was just about the fact that on my P30 pro in videos the background is sometimes edgy no matter on Youtube or Netflix and I was told that it would be because of the video's commission.
Yes it does, but this is probably due to the fact that your internet connection is too slow or Netflix and Youtube think that your connection is too slow. So I have not seen this with HD films yet and I do not think that the producers themselves compress the film so strongly. The content producers do this to the required minimum quality or better quality. Isn't that the Netflix that is not controlled, on the contrary, there are the meticulous pixel counters and the delivered resolution is measured to avoid that in the end only FHD material that is upscaled in the post is cheered.
For example, Arri changed the sensor and the readout method for Netflix and converted old Alexa and Amira to "Netflix versions". The problem is the download or its bandwidth if you use stream because delivery quality or QoS Quality of Service is mostly used. Movies Anime Free. Too much compression what are the consequences?
Ja JadeNation. Ce Centvaughn. That does not happen. Use Wi-Fi. Ha Hallie9. The quality of the footage just gets bad. Ta Tanker The reason was missing for Netflix by pixels with the required DCI4K minimum image width or otherwise UHD resolution is not sufficient for Netflix The problem is the download or its bandwidth if you use stream because delivery quality or QoS Quality of Service is mostly used.Webpack 5 cache
Fake data for Netflix. What can this have for consequences? Qu QuicksandStaking. Are all consequences of SAO on Netflix? Je Jeremy. What is that between us -? Mi Mine Other consequences of you against the wild? Al Alertplucky.
Is that too much of spending on entertainment exaggerated? Ho Horribledress. Iphone 11 uses too much battery?GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.
If nothing happens, download the GitHub extension for Visual Studio and try again. Encodes targeting certain metric values are performed and results stored in respective database files, for example:. The script takes one argument:. BD rates are printed for both as well as subsampling.
It should be noted that BD rate provides one aggregated number over the entire range of target qualities. Another example is, let's say BD rate is zero. And arguably, results from the lower quality region might be immaterial.
The insights described in b thus augment the "overall" insight afforded by BD rate. Given the system you are running on, reasonable concurrency might be limited by number of processor cores or amount of RAM available versus memory consumed by the most demanding encoder process in the ensemble of codecs being tested.
Ideally, an encoder implementation consumes YUV input and generates a codestream. Ideally, a decoder implementation consumes the codestream and decodes to YUV output.
We then compute metrics in YUV space. The extra conversion steps, compared to the regular pipeline, can introduce slight distortion but in our experiments those steps do not make any noticeable dent in the VMAF score.Diagram based 2005 subaru outback trailer wiring
Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Image Compression Comparison Framework.
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