Performance evaluation of digital still camera image processing pipelines

Although its lens and image sensor fundamentally limit a digital still camera's imaging performance, image processing can significantly improve the perceived quality of the output images. A well-designed processing pipeline achieves a good balance between the available processing power and the image yield (the fraction of images that meet a minimum quality criterion). This paper describes the use of subjective and objective measurements to establish a methodology for evaluating the image quality of processing pipelines. The test suite contains images both of analytical test targets for objective measurements, and of scenes for subjective evaluations that cover the photospace for the intended application. Objective image quality metrics correlating with perceived sharpness, noise, and color reproduction were used to evaluate the analytical images. An image quality model estimated the loss in image quality for each metric, and the individual metrics were combined to estimate the overall image quality. The model was trained with the subjective image quality data. The test images were processed through different pipelines, and the overall objective and subjective data was assessed to identify those image quality metrics that exhibit significant correlation with the perception of image quality. This methodology offers designers guidelines for effectively optimizing image quality.