Compressed Image Quality Assessment Based on Saak Features
This addresses the need for improved quality assessment in image compression applications, offering incremental advancements for optimizing image processing algorithms.
The paper tackles the problem of assessing compressed image quality by proposing an objective algorithm that uses Saak features to measure distortions based on human visual importance, achieving better correlation with subjective results and more robust performance across datasets compared to state-of-the-art methods.
Compressed image quality assessment plays an important role in image services, especially in image compression applications, which can be utilized as a guidance to optimize image processing algorithms. In this paper, we propose an objective image quality assessment algorithm to measure the quality of compressed images. The proposed method utilizes a data-driven transform, Saak (Subspace approximation with augmented kernels), to decompose images into hierarchical structural feature space. We measure the distortions of Saak features and accumulate these distortions according to the feature importance to human visual system. Compared with the state-of-the-art image quality assessment methods on widely utilized datasets, the proposed method correlates better with the subjective results. In addition, the proposed methods achieves more robust results on different datasets.