ABS-CSV_VAE: a compressed image quality optimization and feature enhancement method based on singular value decomposition

Abstract Image compression techniques are essential for efficient storage and transmission; however, they often lead to significant degradation in image quality, including the loss of details and the introduction of visual artifacts. The research proposes a novel approach for optimizing compressed image quality and enhancing features using Singular Value Decomposition (SVD) combined with Deep Learning (DL). The objective is to address the limitations of traditional compression methods, which frequently compromise crucial image elements such as edges, textures, and contrast. In the proposed method, the input image is first processed using SVD to extract its fundamental structural components. A threshold-based modification is then applied to the singular values to enhance the representation of low contrast and low resolution features, commonly observed in compressed surveillance images. The images are resized and denoised using a Bilateral Filter to standardize dimensions for uniform processing. Subsequently, the Adaptive Bird Swarm-driven Compressed Singular Value-Enriched VariationalAutoencoder (ABS-CSV-VAE) technique is used to increase the compression ratio and decrease redundancy without significant loss of perceptual quality. The performance of the proposed ABS-CSV-VAE method is evaluated using a dataset of low quality images. Quantitative metrics such as loss (0.8223), PSNR (34.474 dB), SSIM (0.895), BPP (0.4134), parameter count (700 K), and MACs of 51.32 G. The results indicate that the proposed method achieves a PSNR improvement compared to traditional algorithms while restoring key facial features more effectively. Experimental findings confirm that the technique enhances the visual quality of compressed images and preserves essential features, making it highly suitable for applications in video surveillance, biometric recognition, and low bandwidth image transmission systems.