Jens Preiss

Image-Difference Prediction: From Grayscale to Color

Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.

Color-Image Quality Assessment: From Prediction to Optimization

While image-difference metrics show good prediction performance on visual data, they often yield artifact-contaminated results if used as objective functions for optimizing complex image-processing tasks. We investigate in this regard the recently proposed color-image-difference (CID) metric particularly developed for predicting gamut-mapping distortions. We present an algorithm for optimizing gamut mapping employing the CID metric as the objective function. Resulting images contain various visual artifacts, which are addressed by multiple modifications yielding the improved color-image-difference (iCID) metric. The iCID-based optimizations are free from artifacts and retain contrast, structure, and color of the original image to a great extent. Furthermore, the prediction performance on visual data is improved by the modifications.

Evaluating the multi-scale iCID metric

In this study, we investigate the extent to which an image-difference metric based on structural similarity can correlate with human judgment. We introduce a modified version of the recently published iCID metric and present new results over two large image quality databases. It is particularly noteworthy that the proposed metric yields a correlation of 0.861 with mean opinion scores on the 2013 version of the renowned Tampere Image Database, without dedicated parameter optimization.

Gamut mapping in a high-dynamic-range color space

In this paper, we present a novel approach of tone mapping as gamut mapping in a high-dynamic-range (HDR) color space. High- and low-dynamic-range (LDR) images as well as device gamut boundaries can simultaneously be represented within such a color space. This enables a unified transformation of the HDR image into the gamut of an output device (in this paper called HDR gamut mapping). An additional aim of this paper is to investigate the suitability of a specific HDR color space to serve as a working color space for the proposed HDR gamut mapping. For the HDR gamut mapping, we use a recent approach that iteratively minimizes an image-difference metric subject to in-gamut images. A psychophysical experiment on an HDR display shows that the standard reproduction workflow of two subsequent transformations – tone mapping and then gamut mapping – may be improved by HDR gamut mapping.