论文标题
下一代VVC编码和解码的复杂性分析
Complexity Analysis Of Next-Generation VVC Encoding and Decoding
论文作者
论文摘要
尽管下一代视频压缩标准Versatile Video Coding(VVC)提供了出色的压缩效率,但其计算复杂性显着提高。本文通过量化每个编码工具的复杂性分解并测量VVC编码/解码的复杂性和内存需求,对VVC测试模型6的编码器和解码器进行了彻底分析。这些广泛的分析是针对低 - 延迟(LD),随机访问(RA)和All-Intra(AI)条件(总计320个编码/解码)的六个视频序列,为720p,1080p和2160p。结果表明,与LD中的HEVC相比,VVC编码器和解码器分别为5倍和1.5倍,AI中的31倍和1.8倍。编码工具的详细分析表明,在LD中,具有53%的运动估计工具,转换和量化为22%,熵编码为7%,主导了编码复杂性。在解码时,最复杂的模块是最复杂的模块,具有30%的循环过滤器,具有20%的运动补偿和16%的熵解码。此外,通过记忆分析(HEVC的30倍和3X)来测量VVC编码/解码的所需内存带宽。报道的结果和见解是能源有效VVC编码器/解码器的未来研究和实施的指南。
While the next generation video compression standard, Versatile Video Coding (VVC), provides a superior compression efficiency, its computational complexity dramatically increases. This paper thoroughly analyzes this complexity for both encoder and decoder of VVC Test Model 6, by quantifying the complexity break-down for each coding tool and measuring the complexity and memory requirements for VVC encoding/decoding. These extensive analyses are performed for six video sequences of 720p, 1080p, and 2160p, under Low-Delay (LD), Random-Access (RA), and All-Intra (AI) conditions (a total of 320 encoding/decoding). Results indicate that the VVC encoder and decoder are 5x and 1.5x more complex compared to HEVC in LD, and 31x and 1.8x in AI, respectively. Detailed analysis of coding tools reveals that in LD on average, motion estimation tools with 53%, transformation and quantization with 22%, and entropy coding with 7% dominate the encoding complexity. In decoding, loop filters with 30%, motion compensation with 20%, and entropy decoding with 16%, are the most complex modules. Moreover, the required memory bandwidth for VVC encoding/decoding are measured through memory profiling, which are 30x and 3x of HEVC. The reported results and insights are a guide for future research and implementations of energy-efficient VVC encoder/decoder.