  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.
