Saliency Driven Perceptual Image Compression

srikar appalaraju

Abstract:

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.