Non-Reference Quality Monitoring of Digital Images using Gradient Statistics and Feedforward Neural Networks

TL;DR

This paper introduces a non-reference image quality assessment method using gradient statistics and neural networks, suitable for scenarios lacking original images, with promising accuracy and faster computation.

Contribution

It proposes a novel non-reference image quality metric based on gradient statistics and neural networks, effective for image sequences and faster than existing methods.

Findings

Achieved good regression and R2 measures.

Correlations with subjective scores are comparable to state-of-the-art.

Method is computationally faster and suitable for real-time quality assessment.

Abstract

Digital images contain a lot of redundancies, therefore, compressions are applied to reduce the image size without the loss of reasonable image quality. The same become more prominent in the case of videos that contains image sequences and higher compression ratios are achieved in low throughput networks. Assessment of the quality of images in such scenarios becomes of particular interest. Subjective evaluation in most of the scenarios becomes infeasible so objective evaluation is preferred. Among the three objective quality measures, full-reference and reduced-reference methods require an original image in some form to calculate the quality score which is not feasible in scenarios such as broadcasting or IP video. Therefore, a non-reference quality metric is proposed to assess the quality of digital images which calculates luminance and multiscale gradient statistics along with mean subtracted contrast normalized products as features to train a Feedforward Neural Network with Scaled Conjugate Gradient. The trained network has provided good regression and R2 measures and further testing on LIVE Image Quality Assessment database release-2 has shown promising results. Pearson, Kendall, and Spearman's correlation are calculated between predicted and actual quality scores and their results are comparable to the state-of-the-art systems. Moreover, the proposed metric is computationally faster than its counterparts and can be used for the quality assessment of image sequences.