Babu, Venkatesh R and Suresh, S and Perkis, Andrew (2007) No-reference JPEG-image quality assessment using GAP-RBF. In: Signal Processing, 87 (6). pp. 1493-1503.
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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.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Elsevier Science.|
|Keywords:||Image quality;No-reference metric;Blockiness measurement;GAP-RBF;Neural network;JPEG.|
|Department/Centre:||Division of Electrical Sciences > Electrical Engineering|
|Date Deposited:||29 Jul 2008|
|Last Modified:||19 Sep 2010 04:48|
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