MétaCan
Menu
Back to cohort
Record W2890811850 · doi:10.1109/icip.2018.8451018

Low-Delay Hevc Adaptive Quantization Parameter Selection through Temporal Propagation Length Estimation

2018· article· en· W2890811850 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVideo Coding and Compression Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsQuantization (signal processing)Computer scienceCoding (social sciences)AlgorithmPropagation of uncertaintyRate–distortion optimizationReal-time computingMathematical optimizationComputer visionMultiview Video CodingMathematicsStatisticsVideo processing

Abstract

fetched live from OpenAlex

Rate Distortion Optimization (RDO) is employed in the contemporary video coding standard, High Efficiency Video Coding (HEVC), to improve its coding efficiency. Due to its high complexity, RDO is generally performed with fixed quantization parameters (QPs). Fixing QPs, however, does not consider the impact of the current frame on the future frames within the temporal propagation chain, leading to suboptimal performance. To address the adaptive QP design, in this paper, we first estimate the propagation length that is defined as the impact length of the current unit on future units. Based on this impact length, we then propose an adaptive frame-level QP selection algorithm for the low-delay (LD) HEVC standard. Compared to the default HEVC, our method performs significantly better by achieving -4.71% and -3.93% BD-rate gains for LDP and LDB configurations of HEVC, respectively, at a negligible increase in time overhead.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.280
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it