PROJECT TITLE :

A Reduced Reference Distortion Measure for Performance Improvement of Smart Cameras

ABSTRACT:

Application specific data processing (ASIP) unit in good cameras requires refined image processing algorithms for image quality improvement and extraction of relevant features for image understanding and machine vision. The development in performance and robustness will be achieved by intelligent moderation of the parameters both at algorithm (image resolution, contrast, compression, and thus on) as well as hardware levels (camera orientation, field of view, and thus on). This paper discusses the use of ISO/IEC/IEEE 21451 smart transducer standards for performance improvement of smart cameras. The standardized transducer electronic knowledge sheets (TEDS-by IEEE 21450) give the self description of sensors, of which the calibration details are of important importance to yield a smart and reconfigurable imaging system. This is attainable by exercising intelligent management over the TEDS (good camera) calibration details furthermore automated tuning of algorithm parameters (in ASIP) primarily based on selections by perceptually efficient image quality assessment (IQA) tool. Estimation of distortion based mostly on reduced reference IQA has been highlighted as a reliable methodology for this purpose. The proposed IQA approach uses wavelets for options extraction followed by estimation of luminance, contrast, and divergence parameters to obtain the proposed distortion measure (Q). The computational complexity in the method has been catalyzed using integral image and gradient magnitude approaches. The validation of Q metric is applied by evaluating the image quality for varied sorts of distortions on pictures from Content-based Strategies of Image Quality assessment (CSIQ) and Info Visualization CyberInfrastructure (IVC) databases. Simulation results yield a healthy correlation of Q and therefore the subjective human opinions.


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