Fast Sample Adaptive Offset Jointly Based on HOG Features and Depth Information for VVC in Visual Sensor Networks

Abstract

Visual sensor networks (VSNs) can be widely used in multimedia, security monitoring, network camera, industrial detection, and other fields. However, with the development of new communication technology and the increase of the number of camera nodes in VSN, transmitting and compressing the huge amounts of video and image data generated by video and image sensors has become a major challenge. The next-generation video coding standard—versatile video coding (VVC), can effectively compress the visual data, but the higher compression rate is at the cost of heavy computational complexity. Therefore, it is vital to reduce the coding complexity for the VVC encoder to be used in VSNs. In this paper, we propose a sample adaptive offset (SAO) acceleration method by jointly considering the histogram of oriented gradient (HOG) features and the depth information for VVC, which reduces the computational complexity in VSNs. Specifically, first, the offset mode selection (select band offset (BO) mode or edge offset (EO) mode) is simplified by utilizing the partition depth of coding tree unit (CTU). Then, for EO mode, the directional pattern selection is simplified by using HOG features and support vector machine (SVM). Finally, experimental results show that the proposed method averagely saves 67.79% of SAO encoding time only with 0.52% BD-rate degradation compared to the state-of-the-art method in VVC reference software (VTM 5.0) for VSNs.

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