A Retinal Adaptation Model for HDR Image Compression

High dynamic range (HDR) images are usually used to capture more information of natural scenes, because the light intensity of real world scenes commonly varies in a very large range. Humans visual system is able to perceive this huge range of intensity benefiting from the visual adaptation mechanisms. In this paper, we propose a new visual adaptation model based on the cone-and rod-adaptation mechanisms in the retina. The input HDR scene is first processed in two separated channels (i.e., cone and rod channels) with different adaptation parameters. Then, a simple receptive field model is followed to enhance the local contrast of the visual scene and improve the visibility of details. Finally, the compressed HDR image is obtained by recovering the fused luminance distribution to the RGB color space. Experimental results suggest that the proposed retinal adaptation model can effectively compress the dynamic range of HDR images and preserve local details well.