MSU Video Codecs Comparison 2022 Part 2: Cloud
Seventeen Annual Video-Codecs Comparison by MSU
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| compression.ru |
Lomonosov Moscow State University (MSU) Graphics and Media Lab |
Dubna International
State University |
Institute for Information Transmission Problems, Russian Academy of Science |
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News
- 18.04.2023 Additional charts added
- 14.04.2023 Release of the comparison
Navigation
- Objective results
- Subjective results
- Time deviation
- Download
- Participated services
- Subjectify
- Features
- Thanks
- Contact information
- Subscribe to updates
Objective Results
- The results below are based solely on quality scores and do not take into account encoding speed
- Services with scores closer than ~1% share one place
| 480p Comparison | ||||||||||||||
| H264 | HEVC/AV1 | |||||||||||||
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Best quality YUV-SSIM 6:1:1 |
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Best quality YUV-PSNR avg. MSE 6:1:1 |
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Best quality Y-VMAF 0.6.3 |
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Best quality YUV-VMAF 0.6.3 6:1:1 |
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| 720p Comparison | ||||||||||||||
| H264 | HEVC/AV1 | |||||||||||||
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Best quality YUV-SSIM 6:1:1 |
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Best quality YUV-PSNR avg. MSE 6:1:1 |
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Best quality Y-VMAF 0.6.3 |
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Best quality YUV-VMAF 0.6.3 6:1:1 |
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| 1080p Comparison | ||||||||||||||
| H264 | HEVC/AV1 | |||||||||||||
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Best quality YUV-SSIM 6:1:1 |
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Best quality YUV-PSNR avg. MSE 6:1:1 |
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Best quality Y-VMAF 0.6.3 |
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Best quality YUV-VMAF 0.6.3 6:1:1 |
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The winners may vary for different objective quality metrics. The participants were rated using BSQ-rate (enhanced BD-rate) scores [1].
[1] A. Zvezdakova, D. Kulikov, S. Zvezdakov, D. Vatolin, "BSQ-rate: a new approach for video-codec performance comparison and drawbacks of current solutions," 2020.
Subjective Results
- The results below are based solely on quality scores and do not take into account encoding speed
- Services with scores closer than ~1% share one place
| 1080p Subjective Comparison | ||||||||||||||
| H264 | HEVC/AV1 | |||||||||||||
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Best quality YUV-Subjective |
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For subjective quality measurements we used Subjectify.us crowdsourcing platform. We involved more than 10,800+ participants
Encoding time deviation
We performed three encodes for each sequence on different days and day times. The chart below shows a deviation of encoding time among all videos for each iteration. Big delays could be caused by high load of service resources (and big queues) or long time of accessing our videos from storage (the table with services description above shows which storage was used for each service).
Download and buy report
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Cloud Report Objective and subjective comparison of cloud encoding services Released on April, 14 Full version for free |
10 cloud encoding services Alibaba Media Processing Service, AWS Elemental MediaConvert, Azure, Bitmovin, Coconut, Quencode, Tencent MPS-V265, Tencent MPS-O264, Tencent MPS-TXAV1, Zencoder |
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15 videos with different resolutions 1080p, 720p, 480p |
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28+ objective metrics VMAF, SSIM, MS-SSIM, PSNR of different variants |
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HTML report (ZIP) 5400+ interactive charts |
Participated services
| Codec names | Use cases | Storage | |
| 1 |
Alibaba Media Processing Service
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H264, HEVC | Alibaba Cloud OSS |
| 2 |
Tencent MPS-O264
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H264 | Tencent Cloud COS |
| 3 |
Tencent MPS-V265
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HEVC | Tencent Cloud COS |
| 4 |
Tencent MPS-XAV1
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AV1 | Tencent Cloud COS |
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AWS Elemental MediaConvert
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H264, HEVC, AV1 | Amazon S3 (us-east-1) |
| 6 |
Azure
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H264, HEVC | Amazon S3 (us-east-1) |
| 7 |
Bitmovin
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H264, HEVC | Amazon S3 (us-east-1) |
| 8 |
Coconut
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H264, HEVC | Amazon S3 (us-east-1) |
| 9 |
Quencode
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H264, HEVC | Amazon S3 (us-east-1) |
| 10 |
Zencoder
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H264, HEVC, AV1 | Amazon S3 (us-east-1) |
Subjective Comparison Methodology
For subjective quality measurements we used Subjectify.us crowdsourcing platform. We involved 10,800+ participants. After deleting replies from bots we got 529,171 pairwise answers. Bradley-Terry model was used to compute global rank.
To conduct an online crowdsourced comparison, we uploaded encoded streams to Subjectify.us. For better browser compatibility we performed transcoding with x264 and CRF=16.
The platform hired study participants and showed the upload streams to them in pairs. Each pair consisted of two variants of the same test video sequence encoded by various codecs at various bitrates. Videos from each pair were presented to study participant sequentially (i.e., one after another) in full-screen mode. After viewing each pair, participants were asked to choose the video with the best visual quality. They also had the option to play the videos again or to indicate that the videos have equal visual quality. We assigned each study participant 12 pairs, including 2 hidden quality-control pairs, and each received money reward after successfully completing the task. The quality-control pairs consisted of test videos compressed by the x264 encoder at 1 Mbps and 4 Mbps. Responses from participants who failed to choose the 4 Mbps sequence for one or more quality-control questions were excluded from further consideration.
In total we collected 529,171 valid answers from 10,800+ unique participants. To convert the collected pairwise results to subjective scores, we used the Bradley-Terry model [1]. Thus, each codec run received a quality score. We then linearly interpolated these scores to get continuous rate-distortion (RD) curves, which show the relationship between the real bitrate (i.e., the actual bitrate of the encoded stream) and the quality score. Section "RD Curves" shows these curves.
We obtained the subjective scores for this study using Subjectify.us. This platform enables researchers and developers to conduct subjective comparisons of image and video processing methods (e.g., compression, inpainting, denoising, matting, etc.) and carry out studies of human quality perception.
To conduct a subjective study, researchers must apply the methods under comparison to a set of test videos (images), upload the results to Subjectify.us and write a task description for study participants. Subjectify.us handles all the laborious steps of a crowdsourced study: it recruits participants, presents uploaded content in a pairwise fashion, filters out responses from participants who cheat or are careless, analyzes collected results, and generates a study report with interactive plots. Thanks to the pairwise presentation, researchers need not invent a quality scale, as study participants just select the best option of the two.
The platform is optimized for comparison of large video files: it prefetches all videos assigned to a study participant and loads them into his or her device before asking the first question. Thus, even participants with a slow Internet connection wont experience buffering events that might affect quality perception.
To try the platform in your research project, reach out to www.subjectify.us. This demo video shows an overview of the Subjectify.us workflow.
Codec Analysis and Tuning for Codec Developers and Codec Users
Computer Graphics and Multimedia Laboratory of Moscow State University:
- 17+ years working in the area of video codec analysis and tuning using objective quality metrics and subjective comparisons.
- 30+ reports of video codec comparisons and analysis (H.265, H.264, AV1, VP9, MPEG-4, MPEG-2, decoders' error recovery).
- Methods and algorithms for codec comparison and analysis development, separate codec's features and codec's options analysis.
Strong and Weak Points of Your Codec
- Deep encoder parts analysis (ME, RC on GOP, mode decision, etc).
- Weak and strong points for your encoder and complete information about encoding quality on different content types.
- Encoding Quality improvement by the pre and post filtering (including technologies licensing).
Independent Codec Estimation Comparing to Other Codecs for Different Use-cases
- Comparative analysis of your encoder and other encoders.
- We have direct contact with many codec developers.
- You will know place of your encoder between other newest well-known encoders (compare encoding quality, speed, bitrate handling, etc.).
Encoder Features Implementation Optimality Analysis
We perform encoder features effectiveness (speed/quality trade-off) analysis that could lead up to 30% increase in the speed/quality characteristics of your codec. We can help you to tune your codec and find best encoding parameters.Thanks
Special thanks to the following contributors of our previous comparisons
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Contact Information
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Other Materials
Video resources:
Server size: 8069 files, 1215Mb (Server statistics)
Project updated by
Server Team and
MSU Video Group
Project sponsored by YUVsoft Corp.
Project supported by MSU Graphics & Media Lab