An improved R-λ rate control model based on joint spatial-temporal domain information and HVS characteristics
Autor: | Sun Weiheng, Xiong Shuhua, Zeming Zhao, Zhang Feiran, Xiaohai He |
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Rok vydání: | 2020 |
Předmět: |
Least mean squares filter
Computer Networks and Communications Hardware and Architecture Computer science Human visual system model 0202 electrical engineering electronic engineering information engineering Media Technology Rate control 020207 software engineering 02 engineering and technology Algorithm Software |
Zdroj: | Multimedia Tools and Applications. 80:345-366 |
ISSN: | 1573-7721 1380-7501 |
DOI: | 10.1007/s11042-020-09721-9 |
Popis: | With the popularization of smart terminals and multimedia technologies, the video coding standard — H.264/Advanced Video Coding (AVC) and H.265/High Efficiency Video Coding (HEVC) have been unable to meet the needs of various high-definition videos, so the next generation standard —H.266/ Versatile Video Coding (VVC) is under study. In the actual transmission of a video communication channel, rate control plays an important role. However, HEVC rate control based on R-λ model does not adequately take into account the characteristics of the human visual system (HVS). Also, the convergence speed of Least Mean Square (LMS) in HEVC is too slow. In this paper, an improved R-λ(Lambda) rate control model based on joint spatial-temporal domain information and HVS characteristics (IRLRC) is established. In this model, the joint spatial-temporal domain information based on gradient information is used to guide bit allocation for frame and CTU level, where the temporal coefficient is corrected adaptively. What’s more, the Broyden Fletcher Goldfarb Shanno (BFGS) algorithm is introduced, which speeds up the convergence of the proposed model. The experimental results have clearly shown that the proposed IRLRC can achieve better coding performance than HEVC, VVC and other models. In particular, the video sequence based on the proposed IRLRC can meet the needs of HVS and achieve higher optimization for subjective quality. |
Databáze: | OpenAIRE |
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