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Frame-level Bit Allocation Optimization Based on<?brk?> Video Content Characteristics for HEVC

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Published:04 March 2020Publication History
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Abstract

Rate control plays an important role in high efficiency video coding (HEVC), and bit allocation is the foundation of rate control. The video content characteristics are significant for bit allocation, and modeling an accurate relationship between video content characteristics and bit allocation is essential for bit allocation optimization. Therefore, in this article, a video content characteristics–based frame-level optimal bit allocation algorithm is proposed for improving the rate distortion (RD) performance of HEVC. First, the number of search points of motion estimation is used to evaluate the motion activity of video content, and the relationship between the search points and bit allocation is modeled as the search-points model. Second, the grey level co-occurrence matrix and temporal perceptual information are used to evaluate the spatial and temporal texture complexity, and the relationship between the video content texture complexity and bit allocation is modeled as the texture-complexity model. Then, the search-points model and texture-complexity model are jointly employed to allocate the coding bits for the second and third layers of the HEVC hierarchical coding structure. Finally, the remaining coding bits of a group-of-pictures (GOP) are allocated to the first layer of HEVC coding structure. To evaluate the performance of the proposed algorithm, the RD performance and bitrate accuracy are used as evaluation criteria, and the experimental results show that when compared with the popularly used R-λ model–based bit allocation algorithm, the proposed algorithm achieves an average of -3.43% BDBR reduction and 0.13 dB BDPSNR gains with only 0.02% loss of bitrate accuracy.

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 1
      February 2020
      363 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3384216
      Issue’s Table of Contents

      Copyright © 2020 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 4 March 2020
      • Accepted: 1 December 2019
      • Revised: 1 November 2019
      • Received: 1 February 2018
      Published in tomm Volume 16, Issue 1

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