Image Quality Measurement Using Sparse Extreme Learning Machine Classifier

In this paper, we present a machine learning approach to measure the visual quality of JPEG-coded images. 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. The subjective test scores for modified images are obtained with-out referring to their original images (called 'no reference'). Here, the problem of quality estimation is transformed to a sparse data classification problem using a sparse extreme learning machine (S-ELM). The S-ELM classifier estimate the posterior probability of a given image. Here, the mean opinion score ('visual quality') of an image is derived using the predicted class number and their estimated posterior probability. The experimental results prove that the estimated visual quality emulate the mean opinion score very well. The experimental results are compared with the existing JPEG no-reference image quality index and full-reference structural similarity image quality index. The result clearly shows the machine learning approach outperform the existing algorithms in the literature