No-Reference Sharpness Metric based on Local Gradient Analysis

A no-reference perceptual sharpness metric is proposed for sharpness assessment as overall quality indicator in image and video. We also introduce an alternative sharpness metric, able to assess perceived image sharpness, insensible to local blur distortions. The essential idea of our work is to analyze the edge spread in images and the affect on human blur perception. Evaluation is performed by using several quality databases and appendant subjective scores. By comparing with state-of-the-art no-reference sharpness metrics we show the advantage of our approach. The proposed algorithms correlate well with the subjective quality ratings. On uniformly blurred content the metric is even competitive to well-established full-reference metrics. We demonstrate stable results for images with diverse content and show the superiority of the proposed metric compared to latest no-reference metrics in the literature. Another advantage of our approach is the low computational complexity, making real-time video sharpness estimation possible. The experiments were conducted on distorted images, including Gaussian blur, JPEG-2000 compression and white Gaussian noise. The research and implementation of this work has been carried out at JOANNEUM RESEARCH Forschungsgesellschaft mbH, DIGITALInstitute for Information and Communication Technologies. Prototyping was performed in MATLAB and a method to measure the sharpness in video frames was implemented in C++.