2020/02/20 by Keng-Shih Lu, Lu, Keng-Shih, Antonio Ortega +5 · 1 citation
Computer Science · #FOS: Electrical engineering #Image Enhancement Techniques #Image and Video Processing (eess.IV) #Image and Video Quality Assessment #Visual Attention and Saliency Detection #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2002.08558
openalex publication_date 2020/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In image and video coding applications, distortion has been traditionally measured using mean square error (MSE), which suggests the use of orthogonal transforms, such as the discrete cosine transform (DCT). Perceptual metrics such as Structural Similarity (SSIM) are typically used after encoding, but not tied to the encoding process. In this paper, we consider an alternative framework where the goal is to optimize a weighted MSE metric, where different weights can be assigned to each pixel so as to reflect their relative importance in terms of perceptual image quality. For this purpose, we propose a novel transform coding scheme based on irregularity-aware graph Fourier transform (IAGFT), where the induced IAGFT is orthogonal, but the orthogonality is defined with respect to an inner product corresponding to the weighted MSE. We propose to use weights derived from local variances of the input image, such that the weighted MSE aligns with SSIM. In this way, the associated IAGFT can achieve a coding efficiency improvement in SSIM with respect to conventional transform coding based on DCT. Our experimental results show a compression gain in terms of multi-scale SSIM on test images.