A colorization-based anisotropic variational model for vector-valued image compression. (English) Zbl 07675892
Summary: Image compression is an important technology in digital image processing. 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.
MSC:
| 68U10 | Computing methodologies for image processing |
| 94A08 | Image processing (compression, reconstruction, etc.) in information and communication theory |
| 32A70 | Functional analysis techniques applied to functions of several complex variables |
Keywords:
vector-valued image; image compression; anisotropic diffusion; energy functional; alternating minimization methodReferences:
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