An Efficient Multi-Scale Feature Compression With QP-Adaptive Feature Channel Truncation for Video Coding for Machines

An Efficient Multi-Scale Feature Compression With QP-Adaptive Feature Channel Truncation for Video Coding for Machines

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Machine vision-based intelligent applications that analyze video data collected by machines are rapidly increasing.Therefore, it is essential to efficiently compress a large volume of video data for machine consumption.Accordingly, the Moving Picture Experts Group (MPEG) has been developing a new video coding standard called Video Coding for Machines (VCM), aimed at video consumed by machines rather than Record Player with USB Recording humans.Recently, studies have demonstrated that multi-scale feature compression (MSFC)-based feature compression methods significantly improve the performance of MPEG-VCM.This paper proposes an efficient MSFC (eMSFC) method with quantization parameter (QP)-adaptive feature channel truncation.

The proposed eMSFC incorporates an MSFC network with a selective learning strategy (SLS) and Versatile Video Coding (VVC)-based compression.The SLS extracts a single-scale feature from the input image, arranged in order of channel-wise importance.The size of the single-scale feature is adaptively adjusted by truncating the feature channels according to the QP.The truncated feature is efficiently compressed using VVC.Compared Womens dress-shirts to the VCM feature anchor, the experimental results reveal that the proposed method provides a 98.

72%, 98.34%, and 98.04% Bjontegaard delta rate gain for machine vision tasks of instance segmentation, object detection, and object tracking, respectively.The proposed method performed best among the “Call for Evidence” response technologies in MPEG-VCM.