Image coding and compression is one of the most key techniques in the area of image signal processing, However, most of the existing coding methods such as JPEG, employ the similar hybrid architecture to compress images and videos. After many years of development, it is difficult to further improve the coding performance. In addition, most of the existing image compression algorithms are designed to minimize difference between the original and decompressed images based on pixel wise distortion metrics, such as MSE, PSNR which do not consider the HVS features and is not able to guarantee good perceptual quality of reconstructed images, especially at low bit-rate scenarios. In this paper, we propose a novel scheme for low bit-rate image compression. Firstly, the original image is quantized to a binary image based on heat transfer theory. Secondly, the bit sequence of the binary image is divided into several sub-sets and each one is designated a priority based on the rate-distortion principle. Thirdly, the sub-sets with high priorities are selected based on the given bit-rate. Finally, the context-based binary arithmetic coding is employed to encode the sub-sets selected to produce the final compressed stream. At decoder, the image is decoded and reconstructed based on anisotropic diffusion. Experiments are conducted and provide convincing results.
