This paper proposes a quantitative method to measure the color distortion that can occur in color images. There are two main types of color distortion, false color and decolorization. Traditionally, the demosaic process of converting bayer to RGB might results in color distortion, but up-to-date complex color filter array(CFA) can cause even more severe color distortion. Since the conventional method of measuring color distortion requires a reference image, it is difficult to measure color distortion in a situation where the reference image is not secured. We have developed a comprehensive method based on recovering the undistorted color components corresponding to ground truth. Our method uses a chart designed for this purpose and evaluates the color distortion based on this chart.
Objective quality assessment of compressed images is very useful in many applications. In this paper we present an objective quality metric that is better tuned to evaluate the quality of images distorted by compression artifacts. A deep convolutional neural networks is used to extract features from a reference image and its distorted version. Selected features have both spatial and spectral characteristics providing substantial information on perceived quality. These features are extracted from numerous randomly selected patches from images and overall image quality is computed as a weighted sum of patch scores, where weights are learned during training. The model parameters are initialized based on a previous work and further trained using content from a recent JPEG XL call for proposals. The proposed model is then analyzed on both the above JPEG XL test set and images distorted by compression algorithms in the TID2013 database. Test results indicate that the new model outperforms the initial model, as well as other state-of-the-art objective quality metrics.