Perceptual quality metrics applied to still image compression
Signal Processing - Special issue on image and video quality metrics
A survey of hybrid MC/DPCM/DCT video coding distortions
Signal Processing - Special issue on image and video quality metrics
An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A distortion measure for blocking artifacts in images based on human visual sensitivity
IEEE Transactions on Image Processing
A de-blocking algorithm and a blockiness metric for highly compressed images
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
A perceptually relevant no-reference blockiness metric based on local image characteristics
EURASIP Journal on Advances in Signal Processing
Detecting visually similar Web pages: Application to phishing detection
ACM Transactions on Internet Technology (TOIT)
Structural similarity image quality reliability
Signal Processing
Machine learning to design full-reference image quality assessment algorithm
Image Communication
Hi-index | 0.08 |
In this paper, we present a novel no-reference (NR) method to assess the quality of JPEG-coded images using a sequential learning algorithm for growing and pruning radial basis function (GAP-RBF) network. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the functional relationship is approximated using GAP-RBF network. The advantage of using sequential learning algorithm is its capability to learn new samples without affecting the past learning. Further, the sequential learning algorithm requires minimal memory and computational effort. Experimental results prove that the prediction of the trained GAP-RBF network does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.