In Versatile Video Coding (VVC), rate-distortion optimized quantization (RDOQ) is a widely adopted technique to strike a balance between bit rate and distortion. However, the computational complexity introduced by RDO...
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The rate-distortion optimized quantization (RDOQ) provides an excellent trade-off between rate and distortion in High Efficiency Video Coding (HEVC), leading to inspiring improvement in terms of rate-distortion perfor...
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The rate-distortion optimized quantization (RDOQ) provides an excellent trade-off between rate and distortion in High Efficiency Video Coding (HEVC), leading to inspiring improvement in terms of rate-distortion performance. However, its heavy use imposes high complexity on the encoder in real-world video compression applications. In this paper, we provide a comprehensive review on low complexity quantization techniques in HEVC, including both fast RDOQ and all-zero block detection. In particular, the fast RDOQ relies on rate and distortion models for rate-distortion cost estimation, such that the most appropriate quantized coefficient is selected in a low complexity way. All-zero block detection infers the all-zero block by skipping transform and quantization, in an effort to further reduce the complexity. The relationship between the two techniques is also discussed, and moreover, we also envision the future design of low complexity quantization in the upcoming Versatile Video Coding (VVC) standard.
The rate-distortion optimized quantization (RDOQ) in HEVC has improved the coding efficiency of the conventional uniform scalar quantization (SQ) very much. Since the RDOQ is computationally complex, in this paper, we...
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The rate-distortion optimized quantization (RDOQ) in HEVC has improved the coding efficiency of the conventional uniform scalar quantization (SQ) very much. Since the RDOQ is computationally complex, in this paper, we investigate a way of performing RDOQ more efficiently in HEVC. Based on our statistical observation of non-trivial percentage of transform blocks (TB) for which RDOQ does not change their quantization results of SQ, we design a learning-based quantizer selection scheme which can tell in advance whether RDOQ is expected to modify the quantization levels calculated by SQ. Only those TBs likely to be changed by RDOQ are subject to the actual RDOQ process. For the remaining TBs, we design an improved SQ which adapts the dead-zone interval size and round offset based on coefficient group and entropy coding features. The proposed improved SQ has much lower computational complexity than RDOQ while achieving better coding efficiency than the conventional SQ. The experimental results show that our efficient quantization scheme respectively provides 9% and 34% of encoding and quantization time reduction by selectively performing RDOQ only for 21% of TBs. The average BDBR performances of Y, Cb, and Cr channels are respectively -0.03%, 0.48%, and 0.45%.
The rate-distortion optimized quantization (RDOQ) used in video encoding helps to achieve high compression performance but leads to huge computation. We experimentally observe that in approximately half of the quantiz...
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ISBN:
(纸本)9781728198354
The rate-distortion optimized quantization (RDOQ) used in video encoding helps to achieve high compression performance but leads to huge computation. We experimentally observe that in approximately half of the quantization blocks, RDOQ does not change the quantization results initially obtained by the conventional scalar quantizer. In this context, we design a machine learning-based quantizer selection model which lets an encoder decide whether or not to apply RDOQ process for a given transform block (TB) in advance. Our experiments show that the proposed complexity-efficient quantizer selection model reduces 9% and 35% respectively of the encoding and quantization time with BDBR loss of only 0.03%. The proposed selective quantizer achieves almost the same coding performance of RDOQ applied all the time with only around 20% of its actual usage.
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