An Integrated Approach for Lossless Image Compression Using CLAHE, Two-Channel Encoding and Adaptive Arithmetic Coding

• Published in: SN computer science Vol. 5; no. 5; p. 523
• Main Authors: Kumar, P. R. Rajesh, Prabhakar, M.
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.06.2024; Springer Nature B.V
• Subjects: Adaptive algorithms; Advances in Computational Approaches for Image Processing; Algorithms; Arithmetic coding; Cloud Applications and Network Security; Compression ratio; Computed tomography; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data Structures and Information Theory; Digital imaging; Efficiency; Entropy; Equalization; Histograms; Image coding; Image compression; Image contrast; Image enhancement; Image processing; Image quality; Image reconstruction; Image resolution; Image transmission; Information Systems and Communication Service; Mathematical analysis; Medical imaging; Methods; Multimedia communications; Noise control; Original Research; Pattern Recognition and Graphics; Performance evaluation; Ratios; Redundancy; Software Engineering/Programming and Operating Systems; Vision; Wireless Networks; Lossless image coding; Two-channel coding; Compression ratio; Bit rate; CLAHE; Pre-processing; Coding efficiency; Arithmetic coding
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-024-02866-6

Lossless image compression techniques play a crucial role in preserving image quality while reducing storage space and transmission bandwidth. This paper proposes a novel hybrid integrated method for lossless image compression by combining Contrast Limited Adaptive Histogram Equalization (CLAHE), two-channel encoding, and adaptive arithmetic coding to achieve highly efficient compression without any loss of image information. The first step of the proposed approach involves applying Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the local contrast of the image. This pre-processing step aids in reducing the entropy and increasing the redundancy in the image, creating a more favourable environment for subsequent compression algorithms. Next, the image is divided into two channels: one channel focuses on encoding essential structural information, while the other channel handles the finer details. This segregation leverages the inherent properties of images to improve compression efficiency. To achieve further compression gains, an adaptive arithmetic coding algorithm for encoding the data in each channel is utilized. Adaptive arithmetic coding adapts its probability model during the encoding process, leading to improved compression performance compared to traditional static coding methods. The proposed method offers significant potential in various applications, it is especially crucial in medical imaging, where large volumes of high-resolution images are generated during procedures such as MRI, CT scans, or digital pathology, transmitting high-quality images in resource-constrained environments, and facilitating image processing tasks requiring precise data preservation. CLAHE can be a valuable tool in medical imaging to enhance essential diagnostic information in medical images before compression. By improving contrast and visibility of structures, CLAHE may aid in achieving better compression efficiency and reduce the risk of introducing compression artifacts. To assess the effectiveness of our proposed method, comprehensive experiments are conducted on various benchmark image datasets. The performance evaluation parameters such as compressed image size, compression ratio, coding efficiency, compression gain and bit rate evaluated. The results demonstrate that the proposed approach achieves superior compression ratios while ensuring lossless reconstruction of the original image. The incorporation of CLAHE enhances the compression efficiency by exploiting local image characteristics, while two-channel encoding and adaptive arithmetic coding work synergistically to achieve high compression gains.