IEEE Trans Image Process - An estimation-theoretic framework for spatially scalable video coding.

Tópicos

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Resumo

This paper focuses on prediction optimality in spatially scalable video coding. It draws inspiration from an estimation-theoretic prediction framework for quality (SNR) scalability earlier developed by our group, which achieved optimality by fully accounting for relevant information from the current base layer (e.g., quantization intervals) and the enhancement layer, to efficiently calculate the conditional expectation that forms the optimal predictor. It was central to that approach that all layers reconstruct approximations to the same original transform coefficient. In spatial scalability, however, the layers encode different resolution versions of the signal. To approach optimality in enhancement layer prediction, this paper departs from existing spatially scalable codecs that employ pixel domain resampling to perform interlayer prediction. Instead, it incorporates a transform domain resampling technique that ensures that the base layer quantization intervals are accessible and usable at the enhancement layer despite their differing signal resolutions, which in conjunction with prior enhancement layer information, enable optimal prediction. A delayed prediction approach that complements this framework for spatial scalable video coding is then provided to further exploit future base layer frames for additional enhancement layer coding performance gains. Finally, a low-complexity variant of the proposed estimation-theoretic prediction approach is also devised, which approximates the conditional expectation by switching between three predictors depending on a simple condition involving information from both layers, and which retains significant performance gains. Simulations provide experimental evidence that the proposed approaches substantially outperform the standard scalable video codec and other leading competitors.

Resumo Limpo

paper focus predict optim spatial scalabl video code draw inspir estimationtheoret predict framework qualiti snr scalabl earlier develop group achiev optim fulli account relev inform current base layer eg quantiz interv enhanc layer effici calcul condit expect form optim predictor central approach layer reconstruct approxim origin transform coeffici spatial scalabl howev layer encod differ resolut version signal approach optim enhanc layer predict paper depart exist spatial scalabl codec employ pixel domain resampl perform interlay predict instead incorpor transform domain resampl techniqu ensur base layer quantiz interv access usabl enhanc layer despit differ signal resolut conjunct prior enhanc layer inform enabl optim predict delay predict approach complement framework spatial scalabl video code provid exploit futur base layer frame addit enhanc layer code perform gain final lowcomplex variant propos estimationtheoret predict approach also devis approxim condit expect switch three predictor depend simpl condit involv inform layer retain signific perform gain simul provid experiment evid propos approach substanti outperform standard scalabl video codec lead competitor

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