As the need for a video coding technology for a machine that performs intelligent analysis such as object detection, segmentation, and tracking on massive video data has emerged, MPEG is developing a standard called video coding for machines (VCM). VCM is a standard technology for compression of image/video or its features for performing vision tasks of intelligent machines. In this paper, we propose methods that convert multichannel features extracted from an analysis network of input images into a reordered feature map sequence for enhanced compression using VVC. The proposed methods exploit the correlation between channel feature maps using their mean values and sum of absolute difference (SAD) between feature maps in the reordering. Although the proposed methods do not reach the anchor performance of VCM, it shows better coding performance than compressing the feature without channel reordering.
In recent years, 360 videos have been attracting increasing attention as a new type of video that provide immersive experience. Joint Video Experts Team (JVET), which is developing next generation of video coding standard called Versatile Video Coding (VVC), also deal with the compression of 360 videos in the scope of the standard. In the workflow of 360 video coding of JVET, firstly the 360 video is projected onto the 2D plane with a projection format. Some projection formats have inactive region in the converted 2D plane such as rotated sphere projection (RSP). This paper proposes methods of inactive regions padding to improve coding efficiency and reduce visual artifact. The proposed method of inactive region padding gives -0.07% and -0.07% average coding gain of BD-rate in terms of endto-end S-PSNR and WS-PSNR, respectively. In addition, the visual artifacts along the edges of discontinuous faces could be reduced.
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