Visual distortion sensitivity modeling for spatially adaptive quantization in remote sensing image compression

As remote sensing images are often characterized with strong randomness, weak local correlation, and multiple small targets, the commonly used coarse-granularity subband-level quantization scheme fails to make use of these characteristics; thus, the performance improvements of these methods in literature are often marginal. To address this problem, this letter presents a novel spatially adaptive quantization (SAQ) method for the compression of remote sensing images based on our proposed Visual Distortion Sensitivity (ViDiS) Model. The ViDiS model takes into consideration four ViDiS components, including image luminance, spatial frequency, spatial orientation, and visual masking, to help measure the distortion more consistent to the image quality perceived by human beings. Then, a SAQ scheme is proposed to better exploit the content characteristics of remote sensing images, in which the quantization is conducted on a finer subband block level rather than subband level, with the guidance of the ViDiS model. Experimental results show that the proposed algorithm can preserve better visual quality in low-contrast areas with small targets at a competitive computational cost, which makes it more desirable in compression applications for remote sensing images.