A Survey on Multi-camera Image Quality Measures Analysis

Computer Engineering

Most important measures in image research is the analysis of image quality. Quality analysis of images plays very important role in generation of multi viewed images and for the automatic image development in near future. Image quality measure can be performed by two ways subjective and objective. There are various subjective & objectives quality analysis technics are emerged in past year but these are applicable on single camera images. In multi camera images very less research is carried out for the quality analysis. Multi image is nothing but combining multiple images or events into single image. In multi camera images the quality is depend on various factors like configuration, calibration, features of different cameras used to take the images. In multi camera images we can find two types of distortions like photometric and geometric. This paper deals with the various methods and their results to achieve the quality of multi camera images. Here main focus is on the methods like PSNR, MSSIM & VIF and the results of these methods is compared with MIVQM Multi camera image Vision with quality measure (MIVQM) is calculated by combining indices like spatial motion, luminance and contrast and edge based arrangement. The result and comparison with the other measures, like Peak Signal-to Noise Ratio (PSNR), Mean Structural Similarity (SSIM), and Visual Information Fidelity (VIF) prove that MIVQM surpass other measure to capture the quality of images from multi camera system.

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