Color Image Compression Using Vector Quantization with Fuzzy Logic

Abstract

Image compression is a critical method for minimizing digital image size for efficient storage and transmission, particularly in bandwidth-constrained applications. A new approach for color image compression by incorporating vector quantization (VQ) and fuzzy logic is introduced in this paper with the aim of further improving performance. Vector quantization is another commonly used lossy compression technique in which an image is divided into small blocks and mapped to a prechosen set of representative vectors called codewords, thereby compressing the image considerably. In order to solve the problem of maintaining image quality while compressing it, we apply fuzzy logic to increase the accuracy of the codeword selection mechanism. Using the fuzzy logic rules, we define the selection rules adaptively based on the characteristics of the image in order to achieve maximum balance between compression ratio and image quality. Application of fuzzy logic enables smoother movement from one region of images with similar content to another and eliminates quantization errors characteristic for VQ, especially in regions of high variance of pixel intensity. The new hybrid method was applied to several color images and was proven to surpass the traditional VQ method in terms of compression ratio, PSNR, and SSIM measures. The hybrid method offers an efficient plan for high-compression-quality-image with little computational cost