No reference image quality assessment using blocked-based and frequency domain statistical features: a machine learning approach
Jayashri V. Bagade, Kulbir Singh, Yogesh Dandawate · International Journal of Communication Networks and Distributed Systems · 2013
Images are compressed using lossy compression for fast transmission and efficient storage. Due compression artefacts quality of images are degraded. In web application, unavailability of an original image is a major challenge to evaluate quality of images. Therefore there is an immense need to develop a quality metric that will automatically assess quality without referring the original image. In this paper, no reference image quality assessment scheme using the machine learning approach is proposed. The block-based features brightness, contrast, local amplitude, texture and other parameters of the degraded images are calculated along with first order and second order statistical features in frequency domain. These features are given as inputs to well-trained back propagation neural network whose output is a quality score. The mean opinion score is used as target. The result indicates that accuracy of quality assessment is better in comparison with traditional mathematical predictors.