A power-efficient approximate approach to improve the computational complexity of coding tools in versatile video coding

Approximate computing is a technique for optimising algorithms while taking into account both application quality of service and computational complexity. Video encoding is a computationally difficult operation, and the complexity is growing as new compression standards, such as Versatile Video Codi...

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Bibliographic Details
Published in:Multimedia tools and applications 2024-02, Vol.83 (28), p.71071-71087
Main Authors: Shah Oveisi, Sina, Roodaki, Hoda, Rezaalipour, Morteza, Dehyadegari, Masoud
Format: Article
Language:English
Subjects:
Adding circuits
Algorithms
Approximation
Coding
Complexity
Computation
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Multimedia Information Systems
Software
Special Purpose and Application-Based Systems
Video compression
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Summary:Approximate computing is a technique for optimising algorithms while taking into account both application quality of service and computational complexity. Video encoding is a computationally difficult operation, and the complexity is growing as new compression standards, such as Versatile Video Coding (VVC), emerge. Because it uses several new coding methods, the VVC standard delivers superior video compression. However, the human perceptual constraint presents a good chance for a new alternative form of computation, such as approximation computing, which allows for some acceptable level of inaccuracy in the output. In this paper, first, the VVC coding tools are analysed to determine the most computationally complex functions of various coding tools as the best candidates to perform approximate computing. Because addition is a frequent operation employed in all reletaed functions of various coding tools and no one would be feasible without an efficient adder circuit, correct approximate computing is done to the adder circuits of these chosen candidate functions. First, the LOA approximation adder is employed, which achieves 9.67x and 5x power and area reductions, respectively, as compared to the original VVC as the baseline, with no discernible influence on subjective quality. The AxMAP framework is then examined to build more effective approximate adders, which lowered the area and power consumption by 14.88% and 9.11%, respectively, over the LOA technique.
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-18513-4