Nicolas Bonnier

Attributes of image quality for color prints

The evaluation of perceived image quality in color prints is a complex task due to its subjectivity and dimensionality. The perceived quality of an image is influenced by a number of different quality attributes. It is difficult and complicated to evaluate the influ- ence of all attributes on overall image quality, and their influence on other attributes. Because of this difficulty, the most important at- tributes of a color image should be identified to achieve a more efficient and manageable evaluation of the images quality. Based on a survey of the existing literature and a psychophysical experi- ment, we identify and categorize existing image quality attributes to propose a refined selection of meaningful ones for the evaluation of color prints.

Image quality metrics for the evaluation of print quality

Image quality metrics have become more and more popular in the image processing community. However, so far, no one has been able to define an image quality metric well correlated with the percept for overall image quality. One of the causes is that image quality is multi-dimensional and complex. One approach to bridge the gap between perceived and calculated image quality is to reduce the complexity of image quality, by breaking the overall quality into a set of quality attributes. In our research we have presented a set of quality attributes built on existing attributes from the literature. The six proposed quality attributes are: sharpness, color, lightness, artifacts, contrast, and physical. This set keeps the dimensionality to a minimum. An experiment validated the quality attributes as suitable for image quality evaluation. The process of applying image quality metrics to printed images is not straightforward, because image quality metrics require a digital input. A framework has been developed for this process, which includes scanning the print to get a digital copy, image registration, and the application of image quality metrics. With quality attributes for the evaluation of image quality and a framework for applying image quality metrics, a selection of suitable image quality metrics for the different quality attributes has been carried out. Each of the quality attributes has been investigated, and an experimental analysis carried out to find the most suitable image quality metrics for the given quality attributes. For the sharpness attributes the Structural SIMilarity index (SSIM) by Wang et al. (2004) is the the most suitable, and for the other attributes further evaluation is required.

Joint statistical analysis of images and keywords with applications in semantic image enhancement

With the advent of social image-sharing communities, millions of images with associated semantic tags are now available online for free and allow us to exploit this abundant data in new ways. We present a fast non-parametric statistical framework designed to analyze a large data corpus of images and semantic tag pairs and find correspondences between image characteristics and semantic concepts. We learn the relevance of different image characteristics for thousands of keywords from one million annotated images. We demonstrate the frameworks effectiveness with three different examples of semantic image enhancement: we adapt the gray-level tone-mapping, emphasize semantically relevant colors, and perform a defocus magnification for an image based on its semantic context. The performance of our algorithms is validated with psychophysical experiments.

Ink-saving strategy based on document content characterization and halftone textures

Common ink-saving techniques usually restrict the ink consumption when printing a document by replacing a percentage of cyan, magenta, and yellow, by black ink. Even though such methods achieve a considerable reduction in the amount of ink used in a page, the visual quality of the print is affected and unpleasing effects in pastels and skin tones are observed. On the other hand, the quality of the print is not only affected by the ink-saving algorithm, but also by the way the color halftoning algorithm arranges the dots in the print. Therefore, the relationship between the contents of the document to be printed and the printing process needs to be addressed by the ink-saving strategy. A color direct binary search halftoning method that strives to minimize both the ink usage and the perceived error between the continuous-tone color image and the color halftone image is proposed. Our goals are to estimate the effects of the ink-saving module of a printing workflow in individual regions of the document, and to determine the dot arrangement and ink combination that consumes the least amount of ink while preserving printing quality.

Using metrics to assess the ICC perceptual rendering intent

Increased interest in color management has resulted in more options for the user to choose between for their color management needs. We propose an evaluation process that uses metrics to assess the quality of ICC profiles, specifically for the perceptual rendering intent. The primary objective of the perceptual rendering intent, unlike the media-relative intent, is a preferred reproduction rather than an exact match. Profile vendors commonly quote a CIE ΔE*ab color difference to define the quality of a profile. With the perceptual rendering intent, this may or may not correlate to the preferred reproduction. For this work we compiled a comprehensive list of quality aspects, used to evaluate the perceptual rendering intent of an ICC printer profile. The aspects are used as tools to individually judge the different qualities that define the overall strength of profiles. The proposed workflow uses metrics to assess each aspect and delivers a relative comparison between different printer profile options. The aim of the research is to improve the current methods used to evaluate a printer profile, while reducing the amount of time required.