A COLORIZATION-BASED ANISOTROPIC VARIATIONAL MODEL FOR VECTOR-VALUED IMAGE COMPRESSION

Image compression is an important technology in digital image pro-cessing. In this paper, a novel colorization-based codec for vector-valued images is proposed. In compression, we first define the concept of “structure image”, which contains rich geometric structure information of the vector-valued image. Then, to extract representative pixels from the original vector-valued image, a “one-iteration method” is proposed. It can tremendously improve compression efficiency. In decompression, starting from colorizing the structure image, an anisotropic variational model is proposed. The existence and uniqueness of minimizers for the proposed variational model are established. Besides, we develop a fast and efficient algorithm for solving the model numerically by employing the scaled form of the alternating direction method of multipliers (ADMM). Numerical experiments on natural color images demonstrate that the proposed method outperforms the state-of-art colorization-based image compression method. Compared with the transform-based approaches, experiments on satellite multispectral images illustrate that the proposed method is superior to the JPEG and JPEG2000 standards.