# Compressed Image Quality Assessment Based on Saak Features

## TL;DR

This paper introduces a new objective image quality assessment method for compressed images using Saak features, which better correlates with human perception and is robust across datasets.

## Contribution

It presents a novel Saak feature-based approach for image quality assessment that outperforms existing methods in correlation and robustness.

## Key findings

- Better correlation with subjective quality scores
- More robust across different datasets
- Outperforms state-of-the-art methods

## Abstract

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.