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.

As the coronavirus disease 2019 (COVID-19) spreads around the world, industrial automated medical diagnosis systems have been developed, which complete a large amount of medical diagnosis work through computed tomography (CT) images. In these systems, how to quickly store and transmit such a large amount of CT image information has important research significance. In this article, a more targeted COVID-19 chest CT image codec is proposed to make image data not only occupy less space but also have higher image quality. First, the bilateral lung contours are extracted to calculate the position information of the region of interest (ROI). Then, a CT image is classified into four types of nonuniform image blocks according to the characteristics of COVID-19 chest CT images and ROI position information. Next, a series of new transformations are proposed for more efficient transform coding. Finally, a flexible quantization strategy is proposed for the adaptive quantization part. In the experiments, the proposed method is superior to some of the existing methods with similar computational complexity. At the same bit rate, it significantly improves the image quality. This means that chest CT images can still be used for disease diagnosis while taking up less space. In addition, because of the low computational complexity of the proposed method, it can be more easily embedded into the CT equipment with low computational power.

JPEG lossy image compression is a still image compression algorithm model that is currently widely used in major network media. However, it is unsatisfactory in the quality of compressed images at low bit rates. The objective of this paper is to improve the quality of compressed images and suppress blocking artifacts by improving the JPEG image compression model at low bit rates. First, the image texture adaptive non-uniform rectangular partition (ITANRP) algorithm is proposed which partitions the image into$8\times 8$size image blocks with high texture complexity and$16\times 16$size image blocks with low texture complexity. Then, a new transform coding based on the complete orthogonal U-system and all-phase digital filter (APDF) is proposed for coding image blocks with different sizes. Next, a flexible adaptive quantization scheme is designed to quantize image blocks with different sizes by considering the sensitivity of the human visual system (HVS) to different texture complexities. Finally, combining the above method with the JPEG model, a novel image compression algorithm model with low algorithm complexity is proposed to solve the problem in JPEG. The experimental results demonstrate that the performance of our algorithm model outperforms the JPEG image compression algorithms, the quality of the compressed image is greatly improved, and the blocking artifacts are also significantly suppressed.