A method of feature map reordering for machine vision based on channel correlation

Dong-Ha Kim; Yong-Uk Yoon; Jae-Gon Kim

doi:10.1117/12.2626130

30 April 2022 A method of feature map reordering for machine vision based on channel correlation

Dong-Ha Kim, Yong-Uk Yoon, Jae-Gon Kim

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121773E (2022) https://doi.org/10.1117/12.2626130
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

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.

Citation Download Citation

Dong-Ha Kim, Yong-Uk Yoon, and Jae-Gon Kim "A method of feature map reordering for machine vision based on channel correlation", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121773E (30 April 2022); https://doi.org/10.1117/12.2626130

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