Record W2114798828 · doi:10.1109/isit.2009.5205778

A computation approach to the minimum total rate problem of causal video coding

2009· article· en· W2114798828 on OpenAlexaff

En‐hui Yang, Lin Zheng, Zhen Zhang, Dake He

Bibliographic record

Venuenot available

Typearticle

Languageen

FieldEngineering

TopicWireless Communication Security Techniques

Canadian institutionsBlackberry (Canada)University of Waterloo

Fundersnot available

KeywordsFrame (networking)Computer scienceCoding (social sciences)AlgorithmArtificial intelligenceMathematicsStatisticsComputer network

DOI10.1109/isit.2009.5205778

Abstract

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Causal video coding is considered from an information theoretic point of view, where video source frames X 1 , X 2 , ? ? ? X N are encoded in a frame by frame manner, the encoder for each frame X k , k = 1, ? ? ?, N, can use all previous frames and all previous encoded frames while the corresponding decoder can use only all previous encoded frames, and each frame X k itself is modeled as a source X k = {X k (i)} i=1 ? . A novel computation approach is proposed to analytically characterize and numerically compute the minimum total rate R c (D 1 , ? ? ?, D N ) required to achieve a given distortion (quality) level D 1 , ? ? ?, D N ? 0. Specifically, we first show that for jointly stationary ergodic sources X 1 , X 2 , ? ? ?, X N , R c (D 1 , ? ? ?, D N ) is equal to the infimum of the n th order total rate distortion function R c,n (D 1 , ? ? ?, D N ) over all n, where R c,n (D 1 , ? ? ?, D N ) itself is given by the minimum of an information quantity over a set of auxiliary random variables. We then present an iterative algorithm for computing R c,n (D 1 , ? ? ?, D N ) and demonstrate the convergence of the algorithm to the global minimum. The global convergence of the algorithm further enables us to establish a single-letter characterization of R c (D 1 , ? ? ?, D N ) in a novel way when the N sources are an independent and identically distributed vector source. Deep insights from the algorithm are also gained regarding how each frame should be encoded in order to achieve R c (D 1 , ? ? ?, D N ); it is demonstrated by example that R c (D 1 , ? ? ?, D N ) is in general much smaller than the total rate offered by the traditional greedy coding method by which each frame is encoded in a local optimum manner based on all information available to the encoder of the frame. In addition, a tight achievable rate distortion region is also derived.

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.

How this classification was reachedexpand

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: Simulation or modeling

GenreCandidate signal: Empirical · Consensus signal: none

Teacher disagreement score0.817

Threshold uncertainty score0.255

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	0.000	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.

Opus teacher head0.018

GPT teacher head0.249

Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.

Study designSimulation or modeling

Domainnot available

GenreEmpirical

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".

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Citations6

Published2009

Admission routes1

Has abstractyes

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