Efficient VVC Intra Prediction Based on Deep Feature Fusion and Probability Estimation

Zhao, Tiesong; Huang, Yuhang; Feng, Weize; Xu, Yang

doi:10.48550/arxiv.2205.03587

Cited by 1 publication

(1 citation statement)

References 47 publications

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“…The average BDBR increase is 1.25% with a minimum of 0.49 % in "RaceHorses" and the average BDPSNR decrease is 0.095 dB with a minimum of 0.074 dB in "RaceHorses"; therefore, the proposed algorithm can reduce a lot of intra prediction time and has good RD performance. In Table 2, we compare previous fast algorithms [35] on QTMT and efficient intra partitioning based on deep feature fusion and probability estimation [40] with our proposed QPMD method. It is noted that we did not combine the ETOI methods for comparison, owing to the improvement of ISP will greatly affect the results of the comparison of these methods.…”

Section: Objective Experimental Resultsmentioning

confidence: 99%

Saliency-Enabled Coding Unit Partitioning and Quantization Control for Versatile Video Coding

Jiang

Jin

et al. 2022

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The latest video coding standard, versatile video coding (VVC), has greatly improved coding efficiency over its predecessor standard high efficiency video coding (HEVC), but at the expense of sharply increased complexity. In the context of perceptual video coding (PVC), the visual saliency model that utilizes the characteristics of the human visual system to improve coding efficiency has become a reliable method due to advances in computer performance and visual algorithms. In this paper, a novel VVC optimization scheme compliant PVC framework is proposed, which consists of fast coding unit (CU) partition algorithm and quantization control algorithm. Firstly, based on the visual saliency model, we proposed a fast CU division scheme, including the redetermination of the CU division depth by calculating Scharr operator and variance, as well as the executive decision for intra sub-partitions (ISP), to reduce the coding complexity. Secondly, a quantization control algorithm is proposed by adjusting the quantization parameter based on multi-level classification of saliency values at the CU level to reduce the bitrate. In comparison with the reference model, experimental results indicate that the proposed method can reduce about 47.19% computational complexity and achieve a bitrate saving of 3.68% on average. Meanwhile, the proposed algorithm has reasonable peak signal-to-noise ratio losses and nearly the same subjective perceptual quality.

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Section: Objective Experimental Resultsmentioning

confidence: 99%