TY - JOUR
T1 - Content-weighted mean-squared error for quality assessment of compressed images
AU - Gu, Ke
AU - Wang, Shiqi
AU - Zhai, Guangtao
AU - Ma, Siwei
AU - Yang, Xiaokang
AU - Zhang, Wenjun
PY - 2016/7
Y1 - 2016/7
N2 - Image quality assessment (IQA) has been intensively studied, especially for the full-reference (FR) scenario. However, only the mean-squared error (MSE) is widely employed in compression. Why other IQA metrics work ineffectively? We first sum up three main limitations including the computational time, portability, and working manner. To address these problems, we then in this paper propose a new content-weighted MSE (CW-MSE) method to assess the quality of compressed images. The design principle of our model is to use adaptive Gaussian convolution to estimate the influence of image content in a block-based manner, thereby to approximate the human visual perception to image quality. Results of experiments on six popular subjective image quality databases (including LIVE, TID2008, CSIQ, IVC, Toyama and TID2013) confirm the superiority of our CW-MSE over state-of-the-art FR IQA approaches.
AB - Image quality assessment (IQA) has been intensively studied, especially for the full-reference (FR) scenario. However, only the mean-squared error (MSE) is widely employed in compression. Why other IQA metrics work ineffectively? We first sum up three main limitations including the computational time, portability, and working manner. To address these problems, we then in this paper propose a new content-weighted MSE (CW-MSE) method to assess the quality of compressed images. The design principle of our model is to use adaptive Gaussian convolution to estimate the influence of image content in a block-based manner, thereby to approximate the human visual perception to image quality. Results of experiments on six popular subjective image quality databases (including LIVE, TID2008, CSIQ, IVC, Toyama and TID2013) confirm the superiority of our CW-MSE over state-of-the-art FR IQA approaches.
KW - Image compression
KW - Image content
KW - Image quality assessment (IQA)
KW - Mean-squared error (MSE)
UR - http://www.scopus.com/inward/record.url?scp=84944707469&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84944707469&origin=recordpage
U2 - 10.1007/s11760-015-0818-9
DO - 10.1007/s11760-015-0818-9
M3 - RGC 21 - Publication in refereed journal
SN - 1863-1703
VL - 10
SP - 803
EP - 810
JO - Signal, Image and Video Processing
JF - Signal, Image and Video Processing
IS - 5
ER -