No-Reference JPEG Image Quality Assessment Based on Blockiness and Luminance Change
Yibing Zhan, Rong Zhang · IEEE Signal Processing Letters · 2017
When scoring the quality of JPEG images, the two main considerations for viewers are blocking artifacts and improper luminance changes, such as blur. In this letter, we first propose two measures to estimate the blockiness and the luminance change within individual blocks. Then, a no-reference image quality assessment (NR-IQA) method for JPEG images is proposed. Our method obtains the quality score by considering the blocking artifacts and the luminance changes from all nonoverlapping 8 × 8 blocks in one JPEG image. The proposed method has been tested on five public IQA databases and compared with five state-of-the-art NR-IQA methods for JPEG images. The experimental results show that our method is more consistent with subjective evaluations than the state-of-the-art NR-IQA methods. The MATLAB source code of our method is available at http://image.ustc.edu.cn/IQA.html.