Image Compression for Machines Using Boundary-Enhanced Saliency
Yuanyuan Xu, Haolun Lan · 2022
With the rapid development of deep learning, more and more images and videos are used for machine analysis. The amount of images and video content consumed by machines has exceeded that of humans. However, the traditional image and video coding schemes are designed for human vision system, where information that is vital to machine vision, e.g., boundary of a salient object, may not be preserved during compression. In this paper, based on high efficiency video coding (HEVC) intra coding, we propose an image compression scheme for machines using boundary-enhanced saliency. Using image classification as an example task, Grad-CAM, a deep learning visualization method, is used to interpret classification results to generate a pixel-level saliency map for each image. Object segmentation and edge detection are then performed to generate boundary map of the salient object. With boundary-enhanced saliency map, we derive a coding tree unit (CTU)-level QP adjustment scheme, where more bits are allocated to salient regions of image concerning machine vision. Experimental results show that, compared with HEVC, our proposed scheme could achieve up to 29.94% and 31.53% bitrate saving with the same TOP 1 and TOP5 accuracy performance in image classification, respectively.