Reducing image distortions and measuring the excellence in multi-camera images

Abstract

Multi-camera applications are numerous and each application has its specific means of acquisition representation and display. The quality of the perceived multi view video image is dependent on the means of presentation. The most of the fundamental problem in MIQM (Multi-camera Image Quality Measure) is finding the image quality measure. A multi-camera image quality measure MIQM is distortions in multi-camera system can be classified into geometric and photometric distortions. Geometric distortion in multi-camera system is defined as structural disparity such as discontinuity and misalignment in the observed image due to geometric error. Geometric error can occur during mapping which may include rotation and translation. Photometric distortion in single camera is defined as the degradation in perceptual feature that are known to attract visual attention such as noise blur and blocking artifacts. We propose multi-camera image quality measure is combination of the three index measure is necessary to capture the impact of three distortions on multi view perception. The measure was designed to capture the visual effects of artifacts introduced at the acquisition and pre compositing process to predict the composed image quality.