A Comprehensive Literature Review on Image and Video Compression: Trends, Algorithms, and Techniques
Bibliographic record
Abstract
Compression methods for images and videos are essential for the effective archiving, transmission, and distribution of multimedia data and files.This paper reviews the state-ofthe-art in image and video compression, including the most recent developments, algorithms, and methods.This study compiles findings from a variety of studies in an effort to give readers a bird's-eye view of the progress and obstacles in this dynamic sector.A survey of the relevant literature demonstrates that modern compression methods build upon the work of older algorithms like JPEG and MPEG.Compression ratios and picture quality can be enhanced, however, thanks to developments in transform coding, predictive coding, and entropy coding.Further, by combining machine learning and deep learning techniques, we now have access to cutting-edge options for improving compression efficiency and paving the way for adaptive, content-aware compression.Sustainable compression approaches are also highlighted, along with energy efficiency aspects.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".