Paper Info
Title
A novel SSIM index for image quality assessment using a new luminance adaptation effect model in pixel intensity domain
Abstract
The Structural SIMilarity (SSIM) is one of the most prominent image quality assessment (IQA) methods due to its high prediction performance and wide applicability for image quality optimization problems. To reflect the luminance adaptation (LA) characteristic of human visual system (HVS), SSIM is modelled to have high consistency with Weber's law. However, it inevitably has some intrinsic faults that wrongly incorporate the LA effect into SSIM. In this paper, we firstly analyze that Weber's law in the conventional SSIM index cannot precisely reflect the LA effect due to two reasons: (i) it is reported that Weber's law model is not able to precisely be fitted in the measured experimental results for the LA effect; (ii) SSIM is calculated with pixel intensity values, but Weber's law is applied for luminance values which have non-linear relations with the pixel intensity values. To solve this problem, we first theoretically derive a new LA effect model in pixel intensity domain using a Gamma correction function and a power-law model. We then devise a weight function for the LA effect, called LA-based local weight function (LALF) which allows the proposed LA effect model to be precisely incorporated into SSIM index. To verify the effectiveness of the proposed LALF-based SSIM, we perform comprehensive experiments on four large IQA databases. Experimental results show that the proposed LALF helps performance improvement of the SSIM index.
Year
DOI
Venue
2015
10.1109/VCIP.2015.7457810
2015 Visual Communications and Image Processing (VCIP)
Keywords
Field
DocType
human visual system (HVS),image quality assessment (IQA),luminance adaptation (LA)
Computer vision,Weight function,Computer science,Human visual system model,Image quality,Artificial intelligence,Pixel,Luminance,Nonlinear distortion,Image resolution,Gamma correction
Conference
Citations 
PageRank 
References 
2
0.37
12
Authors
2
Name
Order
Citations
PageRank
Sung-Ho Bae118110.54
Munchurl Kim285868.28