A novel vision-based adaptive scanning for the compression of remote sensing images

Most of the compression methods for remote sensing images are often designed under the guidance of mean square error. However, for the vision-related applications, high peak-signal-to-noise ratio (PSNR) does not mean good visual quality. On the other hand, existing compression methods that considering the human visual system (HVS) are usually designed for natural images, without taking the unique characteristics of remote sensing images into account. Focusing on this problem, we present a novel HVS-based adaptive scanning (HAS) scheme for the compression of remote sensing images. First, after the wavelet transform, a retina-based visual sensitivity model is established, and then, the visual weighting mask is generated. Second, for the weighted transformed image, an adaptive scanning method is proposed, which provides different scanning orders among subbands and within a subband, respectively. The former focuses on organizing the codestream according to the importance of weighted subbands, and the latter aims at preserving the direction information of an image as much as possible. Finally, the binary tree codec is utilized. Experimental results show that, as compared with other scan-based compression methods, the proposed HAS-based compression method can provide better visual quality, which makes it more desirable in vision-related applications for remote sensing images.