2/5/2025 | 8:50 AM - 10:30 AM | Grand Peninsula A

Enhancing automatic visual quality in lossy image compression using advanced HVS metrics and adaptive parameter optimization

Author(s)

Oleksandr Zemliachenko | PhD

Abstract

As the demand for high-resolution digital imagery continues to surge, efficient lossy compression techniques that preserve visual fidelity have become increasingly essential. This paper addresses the challenge of automatically achieving specified visual quality levels in the compression of still images and video frames by leveraging advanced human visual system (HVS)-based metrics. We focus on metrics such as PSNR-HVS-M and MSSIM, which more accurately reflect human perception compared to traditional measures like PSNR. An adaptive, iterative compression framework is proposed, enabling automatic adjustment of compression parameters to meet desired visual quality thresholds with minimal computational overhead. Experimental results on a diverse set of grayscale and color images demonstrate that intelligent initialization of compression parameters significantly reduces the number of iterations required. Additionally, we explore cutting-edge non-iterative compression methods, including those utilizing deep neural networks, evaluating their effectiveness in attaining visual quality objectives. Our comprehensive analysis provides valuable insights into the performance of various compression algorithms, offering practical guidelines for their implementation in the evolving field of electronic imaging.