Bibliographic record
Abstract
Video codec compresses the input video source to reduce storage and transmission bandwidth requirements while maintaining the quality.It is an essential technology for applications, to name a few such as digital television, DVD-Video, mobile TV, videoconferencing and internet video streaming.There are different video codecs used in the industry today and understanding their operation to target certain video applications is the key to optimization.The latest advanced video codec standards have become of great importance in multimedia industries which provide cost-effective encoding and decoding of video and contribute for high compression and efficiency.Currently, H.264 AVC, AVS, and DIRAC are used in the industry to compress video.H.264 codec standard developed by the ITU-T Video Coding Experts Group (VCEG) together with the ISO/IEC Moving Picture Experts Group (MPEG).Audio-video coding standard (AVS) is a working group of audio and video coding standard in China.VC-2, also known as Dirac Pro developed by BBC, is a royalty free technology that anyone can use and has been standardized through the SMPTE as VC-2.H.264 AVC, Dirac Pro, Dirac and AVS-P2 are dedicated to High Definition Video, while AVS-P7 is to mobile video.Out of many standards, this work performs a comparative analysis for the H.264 AVC, DIRAC PRO/SMPTE-VC-2 and AVS-P7 standards in low bitrate region and high bitrate region.Bitrate control and constant QP are the methods which are employed for analysis.Evaluation parameters like Compression Ratio, PSNR and SSIM are used for quality comparison.Depending on target application and available bitrate, order of performance is mentioned to show the preferred codec.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".