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Characterization of image distortions in multi-camera systems

Multi-camera systems have been evolving as next generation video cameras with applications including 3D reconstruction, image-based rendering, free viewpoint, and 3D TV. Quantifying visual quality of multi-camera systems is fundamental in developing these applications. In this paper, image distortion types in multi-camera systems are investigated where distortion is classified as either geometric or photometric. Examples and measurements are presented showing that single-view objective image quality measures are not adequate for perceptual assessment of multi-camera images. A new algorithm that characterizes the type of distortion in a given image captured by a multi-camera system is proposed and evaluated. The new algorithm is based on the edge intensity summation (EIS). A new EIS-based structural similarity (EISSM) quality measure is proposed. EISSM is shown to capture the perceptual fidelity that is not fully grasped by PSNR and SSIM.