Blind image quality metric for blackboard lecture images

This paper proposes a reference free perceptual quality metric for blackboard lecture images. The text in the image is mostly affected by high compression ratio and de-noising filters which cause blocking and blurring artifacts. As a result the perceived text quality of the blackboard image degrades. The degraded text is not only difficult to read by humans but it also makes the optical character recognition task even more difficult. Therefore, we put our effort firstly to estimate the presence of these artifacts and then we used it in our proposed quality metric. The blocking and blurring features are extracted from the image content on block boundaries without the presence of reference image. Thus it makes our metric reference free. The metric also uses the visual saliency model to mimic the human visual system (HVS) by focusing only on the distortions in perceptually important regions, i.e. those regions which contains the text. Moreover psychophysical experiments are conducted that show very good correlation between the mean opinion score and quality scores obtained from our reference free perceptual quality metric (RF-PQM). The correlation results are also compared with standard reference and reference free metric.