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Farhad Pakdaman

Farhad Pakdaman

Academic rank: Assistant Professor
ORCID:
Education: PhD.
ScopusId:
HIndex:
Faculty: Faculty of Technology and Engineering
Address:
Phone: 011-35302903

Research

Title
Complexity analysis of next-generation vvc encoding and decoding
Type
Presentation
Keywords
video coding, video decoding, complexity analysis, Versatile Video Coding (VVC), VVC Test Model (VTM)
Year
2020
Researchers Farhad Pakdaman ، Mohammad Ali Adelimanesh ، Moncef Gabbouj ، Mahmoud Reza Hashemi

Abstract

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 5× and 1.5× more complex compared to HEVC in LD, and 31× and 1.8× 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 30× and 3× of HEVC. The reported results and insights are a guide for future research and implementations of energy-efficient VVC encoder/decoder.