Efficient Noise-Aware Image Compression using DWT and Multi-Level Block Coding Technique

Abstract

Efficient image compression in the presence of noise remains a significant challenge in modern image processing applications, particularly in medical imaging, remote sensing, and multimedia transmission. This paper proposes a noise-aware image compression framework that integrates Discrete Wavelet Transform (DWT) with a multi-level block coding strategy to achieve high compression efficiency while preserving image quality. The proposed method first applies DWT to decompose the input image into multiple frequency sub-bands, enabling effective separation of noise components from essential image features. A noise estimation and adaptive thresholding mechanism is then employed to suppress noise in high-frequency coefficients without significantly affecting important structural details. Subsequently, a multi-level block coding technique is introduced, where the transformed coefficients are partitioned into variable-sized blocks based on their energy distribution and perceptual importance. High-energy blocks, containing critical image information, are encoded with higher precision, while low-energy blocks are aggressively compressed to reduce redundancy. This adaptive block-level encoding improves compression ratio while maintaining visual fidelity. The proposed approach also incorporates entropy coding to further enhance compression performance.

Experimental results demonstrate that the proposed method achieves superior performance in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and compression ratio compared to conventional DWT-based compression techniques. The algorithm shows robustness against different types of noise, including Gaussian and impulse noise, making it suitable for real-world applications. Overall, the integration of DWT with multi-level block coding provides an efficient and scalable solution for noise-aware image compression with improved reconstruction quality and reduced storage requirements.