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Application specific information processing (ASIP) unit in smart cameras requires sophisticated image processing algorithms for image quality improvement and extraction of relevant features for image understanding and machine vision. The improvement in performance as well as robustness can be achieved by intelligent moderation of the parameters both at algorithm (image resolution, contrast, compression, and so on) as well as hardware levels (camera orientation, field of view, and so on). This paper discusses the employment of ISO/IEC/IEEE 21451 smart transducer standards for performance improvement of smart cameras. The standardized transducer electronic data sheets (TEDS-by IEEE 21450) provide the self description of sensors, of which the calibration details are of vital importance to yield a smart and reconfigurable imaging system. This is possible by exercising intelligent control over the TEDS (smart camera) calibration details as well as automated tuning of algorithm parameters (in ASIP) based on decisions by perceptually efficient image quality assessment (IQA) tool. Estimation of distortion based on reduced reference IQA has been highlighted as a reliable methodology for this purpose. The proposed IQA approach uses wavelets for features extraction followed by estimation of luminance, contrast, and divergence parameters to obtain the proposed distortion measure (Q). The computational complexity in the process has been catalyzed using integral image and gradient magnitude approaches. The validation of Q metric is carried out by evaluating the image quality for various types of distortions on images from Content-based Strategies of Image Quality assessment (CSIQ) and Information Visualization CyberInfrastructure (IVC) databases. Simulation results yield a healthy correlation of Q and the subjective human opinions.

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Reduced reference image quality assessment (IQA) has been highlighted as a reliable methodology for the purpose of quality-of-service (QoS) monitoring in network visual communication applications; as it requires only selective features of the reference image where complete image cannot be accessed (as reference for IQA). The reduced reference IQA problem has been constrained towards extraction of appropriate features (of reference and transformed images) sensitive to various distortion types along with due coherence with human perception. This paper presents an improved reduced reference IQA approach named as 'ISIM: Image Similarity Metric' based on natural image statistics. The proposed IQA approach involves sub-band decomposition of acquired images for features extraction using wavelets. The extracted features are then used to obtain a global distortion measure (Q) as a function of luminance, contrast and divergence parameters. The computational complexity during features extraction is reduced by employing combination of integral image and gradient magnitude approaches. This distortion measure (Q) is then used to formulate ISIM metric as a linear regression with full-reference SSIM. The validation of the proposed metric is carried out by evaluating the image quality for various types of distortions from CSIQ and IVC databases. Simulation results yield a healthy correlation of proposed ISIM metric and the subjective human opinions along with minimal computational complexity in comparison to other state-of-art reduced reference IQA measures.