In complementary metal oxide semiconductor image sensor (CIS) industry, advances of techniques have been introduced and it led to unexpected artifacts in the photograph. The color dots, known as false color, also appear in images from CIS employing the modified color filter arrays and the remosaicing image signal processors (ISPs). Therefore, the objective metric for image quality assessments (IQAs) have become important to minimize artifacts for CIS manufacturers. In our study, we suggest a novel no-reference IQA metric to quantify the false color occurring in practical IQA scenarios. We propose a pseudo-reference to overcome the absence of reference image, inferring an ideal sensor output. As we detected the distorted pixels by specifying outlier colors with a statistical method, the pseudo-reference was generated while correcting outlier pixels with the appropriate colors according to an unsupervised clustering model. With the derived pseudo-reference, our method suggests a metric score based on the color difference from an input, as it reflects the results of our subjective false color visibility analysis.
In photography, the dynamic range (DR) is a distinguishable brightness range and is determined by the analog-to-digital converter (ADC) and signal-to-noise (SNR) performance of the sensor. Recently, many various HDR strategies have been introduced to obtain high DR beyond these hardware limitations. However, since camera manufacturers set these HDR algorithms to operate differently by considering the situation, it is necessary to evaluate the quality of images taken in various situations for objective evaluation. In order to quantitatively measure the DR, we should know both the actual luminous intensity and the SNR of the picture. However, it is difficult to measure the two information in general-scene photos without charts. To overcome these problems, in this study, we propose a method to measure the DR of a natural-scene photograph by reconstructing radiance map and specifying the pixel value at which the SNR reaches 12dB. Using the pre-calculated radiation and SNR information, we measured DR of photos without using a chart, and demonstrated that HDR images have higher DR than standard DR (SDR) images.
The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply nonlinear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The sceneand- process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-processdependent Modulation Transfer Functions (SPD-MTF).