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Título

Bayesian adaptive algorithm for fast coding unit decision in the High Efficiency Video Coding (HEVC) standard

AutorJimenez-Moreno, A.; Martínez-Enríquez, Eduardo ; Díaz de María, F.
Palabras claveOn the fly estimation
Complexity reduction
Fast coding unit decision
Hypothesis test
Bayesian statistics
High Efficiency Video Coding
Fecha de publicación8-abr-2017
EditorElsevier
CitaciónSignal Processing: Image Communication 56: 1-11 (2017)
ResumenThe latest High Efficiency Video Coding standard (HEVC) provides a set of new coding tools to achieve a significantly higher coding efficiency than previous standards. In this standard, the pixels are first grouped into Coding Units (CU), then Prediction Units (PU), and finally Transform Units (TU). All these coding levels are organized into a quadtree-shaped arrangement that allows highly flexible data representation; however, they involve a very high computational complexity. In this paper, we propose an effective early CU depth decision algorithm to reduce the encoder complexity. Our proposal is based on a hierarchical approach, in which a hypothesis test is designed to make a decision at every CU depth, where the algorithm either produces an early termination or decides to evaluate the subsequent depth level. Moreover, the proposed method is able to adaptively estimate the parameters that define each hypothesis test, so that it adapts its behavior to the variable contents of the video sequences. The proposed method has been extensively tested, and the experimental results show that our proposal outperforms several state-of-the-art methods, achieving a significant reduction of the computational complexity (36.5% and 38.2% average reductions in coding time for two different encoder configurations) in exchange for very slight losses in coding performance (1.7% and 0.8% average bit rate increments).
Descripción11 pags., 9 figs., 6 tabs.
Versión del editorhttps://doi.org/10.1016/j.image.2017.04.004
URIhttp://hdl.handle.net/10261/163258
Identificadoresdoi: 10.1016/j.image.2017.04.004
issn: 0923-5965
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