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Fuzzy SVM-Based Coding Unit Decision in HEVC
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DOI：10.1109/TBC.2017.2762470.png)
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Abstract
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The latest video compression standard, High Efficiency Video Coding (HEVC), has greatly improved the coding efficiency compared to the predecessor H. 264/AVC. However, equipped with the quadtree structure of coding tree unit partition and other sophisticated coding tools, HEVC brings a significant increase in the computational complexity. To address this issue, a coding unit (CU) decision method based on fuzzy support vector machine (SVM) is proposed for rate-distortion-complexity (RDC) optimization, where the process of CU decision is formulated as a cascaded multi-level classification task. The optimal feature set is selected according to a defined misclassification cost and a risk area is introduced for an uncertain classification output. To further improve the RDC performance, different regulation parameters in SVM are adopted and outliers in training samples are eliminated. Additionally, the proposed CU decision method is incorporated into a joint RDC optimization framework, where the width of risk area is adaptively adjusted to allocate flexible computational complexity to different CUs, aiming at minimizing computational complexity under a configurable constraint in terms of RD performance degradation. Experimental results show that the proposed approach can reduce 58.9% and 55.3% computational complexity on average with the values of Bjonteggard delta peak-signal-to-noise ratio as -0.075 dB and -0.085 dB and the values of Bjontegaard delta bit rate as 2.859% and 2.671% under low delay P and random access configurations, respectively, which has outperformed the state-of-the-art fast algorithms based on statistical information and machine learning.
Keywords:
Misclassification cost
fuzzy support vector machine
coding unit decision
rate-distortion-complexity optimization
High Efficiency Video Coding
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