Rate-distortion Optimization for MP3 Audio Coding with Complete Decoder Compatibility
Bibliographic record
Abstract
This paper addresses the issue of truly optimizing the rate-distortion (RD) performance for MPEG-1/2 Layer III (MP3) audio coding by presenting a fixed-slope graph-based iterative algorithm to jointly design quantization, Huffman code-book (HCB) selection and region division. Given quantization step sizes and HCB selection, quantization outputs and HCB region division are first adapted to minimize the Lagrangian RD cost function throughout a standard-constrained graph structure. Then quantization step sizes and HCB selection are optimized serially to reduce the RD cost based on given quantization outputs and region division. These three steps are further alternated until convergence occurs. The proposed algorithm has been implemented based on ISO reference codec and LAME 3.96.1 with complete decoder compatibility. Simulation results show that the new MP3 coders resulting from the proposed algorithm indeed improve the RD performance substantially except for extremely low bit rates in both cases
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".