DNP-AUT: Image Compression Using Double-Layer Non-Uniform Partition and Adaptive U Transform

To provide an image compression method with better compression performance and lower computational complexity, a new image compression algorithm is proposed in this paper. First, a double-layer non-uniform partition algorithm is proposed, which analyzes the texture complexity of image blocks and performs partitioning and merging of the image blocks at different scales to provide a priori information that helps to reduce the spatial redundancy for subsequent compression against the blocks. Next, by considering the multi-transform cores, we propose an adaptive U transform scheme, which performs more specific coding for different types of image blocks to enhance the coding performance. Finally, in order that the bit allocation can be more flexible and accurate, a fully adaptive quantization technique is proposed. It not only formulates the quantization coefficient relationship between image blocks of different sizes but also further refines the quantization coefficient relationship between image blocks under different topologies. Extensive experiments indicate that the compression performance of the proposed algorithm not only significantly surpasses the JPEG but also surpasses some state-of-the-art compression algorithms with similar computational complexity. In addition, compared with the JPEG2000 compression algorithm, which has greater with higher computational complexity, its compression performance also has certain advantages.