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.

中文翻译：

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基于非均匀分区和U系统的图像压缩新算法

JPEG有损图像压缩是一种静止图像压缩算法模型，目前已在主要网络媒体中广泛使用。但是，在低比特率下压缩图像的质量不能令人满意。本文的目的是通过改进低比特率的JPEG图像压缩模型来提高压缩图像的质量并抑制块状伪影。首先，提出了图像纹理自适应非均匀矩形分割（ITANRP）算法，该算法将图像分割为$ 8 /次8 $ 尺寸高，图像复杂度高的图像块 $ 16 \次16 $尺寸低，图像复杂度高的图像块。然后，提出了一种基于完全正交U系统和全相位数字滤波器（APDF）的新变换编码，用于对不同大小的图像块进行编码。接下来，设计一种灵活的自适应量化方案，通过考虑人类视觉系统（HVS）对不同纹理复杂度的敏感性来量化具有不同大小的图像块。最后，将上述方法与JPEG模型相结合，提出了一种算法复杂度较低的图像压缩算法模型，以解决JPEG中的问题。实验结果表明，我们的算法模型的性能优于JPEG图像压缩算法，压缩图像的质量得到了极大的改善，并且块状伪影也得到了显着的抑制。