Fabrizio Marini

Contrast image correction method

A method for contrast enhancement is proposed. The algorithm is based on a local and image-dependent exponential cor- rection. The technique aims to correct images that simultaneously present overexposed and underexposed regions. To prevent halo artifacts, the bilateral filter is used as the mask of the exponential correction. Depending on the characteristics of the image (piloted by histogram analysis), an automated parameter-tuning step is intro- duced, followed by stretching, clipping, and saturation preserving treatments. Comparisons with other contrast enhancement tech- niques are presented. The Mean Opinion Score (MOS) experiment on grayscale images gives the greatest preference score for our algorithm.

Image quality assessment by preprocessing and full reference model combination

This paper focuses on full-reference image quality assessment and presents different computational strategies aimed to improve the robustness and accuracy of some well known and widely used state of the art models, namely the Structural Similarity approach (SSIM) by Wang and Bovik and the S-CIELAB spatial-color model by Zhang and Wandell. We investigate the hypothesis that combining error images with a visual attention model could allow a better fit of the psycho-visual data of the LIVE Image Quality assessment Database Release 2. We show that the proposed quality assessment metric better correlates with the experimental data.

Image quality: a tool for no-reference assessment methods

In this work we propose an image quality assessment tool. The tool is composed of different modules that implement several No Reference (NR) metrics (i.e. where the original or ideal image is not available). Different types of image quality attributes can be taken into account by the NR methods, like blurriness, graininess, blockiness, lack of contrast and lack of saturation or colorfulness among others. Our tool aims to give a structured view of a collection of objective metrics that are available for the different distortions within an integrated framework. As each metric corresponds to a single module, our tool can be easily extended to include new metrics or to substitute some of them. The software permits to apply the metrics not only globally but also locally to different regions of interest of the image.

Image quality assessment in multimedia applications

In the framework of multimedia applications image quality may have different meanings and interpretations. In this paper, considering the quality of an image as the degree of adequacy to its function/goal within a specific application field, we provide an organized overview of image quality assessment methods by putting in evidence their applicability and limitations in different application domains. Three scenarios have been chosen representing three typical applications with different degree of constraints in their image workflow chains and requiring different image quality assessment methodologies.

A sharpness measure on automatically selected edge segments

We address the problem of image quality assessment for natural images, focusing on No Reference (NR) assessment methods for sharpness. The metrics proposed in the literature are based on edge pixel measures that significantly suffer the presence of noise. In this work we present an automatic method that selects edge segments, making it possible to evaluate sharpness on more reliable data. To reduce the noise influence, we also propose a new sharpness metric for natural images.

A no-reference metric for demosaicing artifacts that fits psycho-visual experiments

The present work concerns the analysis of how demosaicing artifacts affect image quality and proposes a novel no-reference metric for their quantification. This metric that fits the psycho-visual data obtained by an experiment analyzes the perceived distortions produced by demosaicing algorithms. The demosaicing operation consists of a combination of color interpolation (CI) and anti-aliasing (AA) algorithms and converts a raw image acquired with a single sensor array, overlaid with a color filter array, into a full-color image. The most prominent artifact generated by demosaicing algorithms is called zipper. The zipper artifact is characterized by segments (zips) with an On–Off pattern. We perform psycho-visual experiments on a dataset of images that covers nine different degrees of distortions, obtained using three CI algorithms combined with two AA algorithms. We then propose our no-reference metric based on measures of blurriness, chromatic and achromatic distortions to fit the psycho-visual data. With this metric demosaicing algorithms could be evaluated and compared.

No-reference metrics for demosaicing

The present work concerns the development of a no-reference demosaicing quality metric. The demosaicing operation converts a raw image acquired with a single sensor array, overlaid with a color filter array, into a full-color image. The most prominent artifact generated by demosaicing algorithms is called zipper. In this work we propose an algorithm to identify these patterns and measure their visibility in order to estimate the perceived quality of rendered images. We have conducted extensive subjective experiments, and we have determined the relationships between subjective scores and the proposed measure to obtain a reliable no-reference metric.

BIO-INSPIRED FRAMEWORK FOR AUTOMATIC IMAGE QUALITY ENHANCEMENT

We propose a bio-inspired framework for automatic image quality enhancement. Restoration algorithms usually have fixed parameters whose values are not easily settable and depend on image content. In this study, we show that it is possible to correlate no-reference visual quality values to specific parameter settings such that the quality of an image could be effectively enhanced through the restoration algorithm. In this paper, we chose JPEG blockiness distortion as a case study. As for the restoration algorithm, we used either a bilateral filter, or a total variation denoising detexturer. The experimental results on the LIVE database will be reported. These results will demonstrate that a better visual quality is achieved through the optimized parameters over the entire range of compression, with respect to the algorithm default parameters.