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HEVC 中的混合全零软量化块检测。

Hybrid All Zero Soft Quantized Block Detection for HEVC.

出版信息

IEEE Trans Image Process. 2018 Oct;27(10):4987-5001. doi: 10.1109/TIP.2018.2837351.

DOI:10.1109/TIP.2018.2837351
PMID:29985138
Abstract

Transform and quantization account for a considerable amount of computation time in video encoding process. However, there are a large number of discrete cosine transform coefficients which are finally quantized into zeros. In essence, blocks with all zero quantized coefficients do not transmit any information, but still occupy substantial unnecessary computational resources. As such, detecting all-zero block (AZB) before transform and quantization has been recognized to be an efficient approach to speed up the encoding process. Instead of considering the hard-decision quantization (HDQ) only, in this paper, we incorporate the properties of soft-decision quantization into the AZB detection. In particular, we categorize the AZB blocks into genuine AZBs (G-AZB) and pseudo AZBs (P-AZBs) to distinguish their origins. For G-AZBs directly generated from HDQ, the sum of absolute transformed difference-based approach is adopted for early termination. Regarding the classification of P-AZBs which are generated in the sense of rate-distortion optimization, the rate-distortion models established based on transform coefficients together with the adaptive searching of the maximum transform coefficient are jointly employed for the discrimination. Experimental results show that our algorithm can achieve up to 24.16% transform and quantization time-savings with less than 0.06% RD performance loss. The total encoder time saving is about 5.18% on average with the maximum value up to 9.12%. Moreover, the detection accuracy of larger TU sizes, such as and can reach to 95% on average.

摘要

变换和量化在视频编码过程中占据了相当大的计算时间。然而,最终有大量的离散余弦变换系数被量化为零。从本质上讲,具有所有零量化系数的块实际上没有传输任何信息,但仍然占用大量不必要的计算资源。因此,在变换和量化之前检测全零块(AZB)已被认为是加速编码过程的有效方法。在本文中,我们不仅考虑了硬决策量化(HDQ),还将软决策量化的特性纳入到 AZB 检测中。具体来说,我们将 AZB 块分为真正的 AZB(G-AZB)和伪 AZB(P-AZB),以区分它们的来源。对于直接由 HDQ 生成的 G-AZB,采用基于绝对变换差和的方法进行早期终止。对于在率失真优化意义上生成的 P-AZB 的分类,我们共同使用基于变换系数的率失真模型和自适应搜索最大变换系数来进行区分。实验结果表明,我们的算法可以在 RD 性能损失小于 0.06%的情况下,实现高达 24.16%的变换和量化时间节省。平均总编码器时间节省约为 5.18%,最大节省值高达 9.12%。此外,对于较大 TU 大小(如和)的检测精度平均可以达到 95%。

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