一种基于曲面拟合的图像压缩方法

An Image Compression Method Based on Surface Fitting

针对使用固定大小图像块和固定模型的曲面拟合图像压缩方法存在的还原图像质量问题,提出了一种多层次多模型的图像数据拟合算法,即首先依据图像区域的平坦度对图像进行多层级的错位分块,然后进一步依据分块的数据平坦度选择不同模型拟合达到压缩数据的目的;在图像还原时,依据分块之间的错位信息进行边界融合,较好消除了分块间的拼接痕迹,有效提高了还原后的图像质量。本文所提算法与经典的多项式拟合算法、标准图像压缩算法(JPEG)以及JPEG2000算法实验对比分析,本文算法有效提高了图像的压缩比,较好消除了还原图像的块效应,且在相同峰值信噪比下,所提算法图像质量与结构相似性指数高于JPEG和JPEG2000算法。

A multi-level,multi-model image data fitting algorithm is proposed to address the issue of image quality in the existing image compression methods that use fixed-size image blocks and fixed models for surface fitting.Firstly,the image is divided into multiple hierarchical displaced blocks based on the flatness of image regions.Then,different models are selected for fitting the blocks based on their data flatness,aiming to achieve data compression.During image reconstruction,boundary fusion is performed using the displacement information between blocks,effectively reducing artifacts at block boundaries and improving the quality of the reconstructed image.The proposed algorithm is compared and analyzed against classical polynomial fitting algorithms,standard image compression algorithms,and JPEG2000.The results show that the proposed algorithm achieves improved compression ratio,effectively mitigates block artifacts in the reconstructed image,and exhibits higher image quality and structural similarity index compared to JPEG and JPEG2000 algorithms under the same peak signal-to-noise ratio.