Recognition-Driven Compressed Image Generation Using Semantic-Prior Information

Image compression, as one of the fundamental low-level image processing tasks, is very essential for computer vision. Current image compression methods can maintain considerable visual quality even at relatively lower bit-rate, but pay little attention to their performance in high-level vision tasks, e.g, image recognition and semantic segmentation. In this letter, we aim to generate compressed images with similar visual quality as before, but with much higher recognition accuracy. To this end, we explore the significance of semantic-prior information in image compression and design a semantic-prior attention module to adaptively enhance the semantic-ware features. Moreover, a semantic perceptual loss combining semantic-prior map is employed to concentrate on the machine perceptual quality of semantic-ware content. Experiments on benchmarks demonstrate that the proposed algorithm has the effectiveness to improve recognition accuracy and maintain visual quality. In addition, performance improvement on different recognition networks and tasks shows the generality of our algorithm.