1 research outputs found
Saliency Driven Perceptual Image Compression
This paper proposes a new end-to-end trainable model for lossy image
compression, which includes several novel components. The method incorporates
1) an adequate perceptual similarity metric; 2) saliency in the images; 3) a
hierarchical auto-regressive model. This paper demonstrates that the popularly
used evaluations metrics such as MS-SSIM and PSNR are inadequate for judging
the performance of image compression techniques as they do not align with the
human perception of similarity. Alternatively, a new metric is proposed, which
is learned on perceptual similarity data specific to image compression. The
proposed compression model incorporates the salient regions and optimizes on
the proposed perceptual similarity metric. The model not only generates images
which are visually better but also gives superior performance for subsequent
computer vision tasks such as object detection and segmentation when compared
to existing engineered or learned compression techniques.Comment: WACV 2021 camera-ready versio