MATLAB-Based Applications for Image Processing and Image Quality Assessment – Part I: Software Description

This paper describes several MATLAB-based applications useful for image processing and image quality assessment. The Image Processing Application helps user to easily modify images, the Image Quality Adjustment Application enables to create series of pictures with different quality. The Image Quality Assessment Application contains objective full reference quality metrics that can be used for image quality assessment. The Image Quality Evaluation Applications represent an easy way to compare subjectively the quality of distorted images with reference image. Results of these subjective tests can be processed by using the Results Processing Application. All applications provide Graphical User Interface (GUI) for the intuitive usage

Influence of High Level Features of HVS on Performance of FSIM

In this paper the influence of information about high level features of Human Visual System (HVS) on objective quality assessment is studied. This was done by extending the existing full-reference objective image quality metric – FSIM – where the different importance of certain areas of image is considered using Phase Congruency (PC) algorithm. Here, the estimation of Region of Interest (ROI) based on this algorithm is complemented by Fixation Density Maps (FDM) containing the information about high level features of HVS. Use of another low level features based algorithm (Phase Spectrum of Fourier Transform) was also considered and compared to the PC algorithm. The performance was evaluated qualitatively on images reconstructed according to ROI and quantitatively on images from LIVE database. The correlation between subjective and objective tests was calculated using Pearson’s Correlation Coefficient and Spearman’s Rank Order Coefficient. The statistical significance of the difference between correlation coefficients was assessed by Fisher r-to-z transformation. The performance of the metric was also compared to other state-of-the-art image quality metrics (SSIM, MS-SSIM, and FSIM)

MATLAB-based Applications for Image Processing and Image Quality Assessment – Part II: Experimental Results

The paper provides an overview of some possible usage of the software described in the Part I. It contains the real examples of image quality improvement, distortion simulations, objective and subjective quality assessment and other ways of image processing that can be obtained by the individual applications

Comparison of Metrics for Predicting Image and Video Quality at Varying Viewing Distances

Viewing distance and display resolution have ar-guably a significant impact on perceived image quality; images seen on a mobile phone with high pixel density reveal fewer distortions than the same images seen on a large TV from a close distance. However, only a few image and video quality metrics account for the effect of viewing distance and resolution. Those that do, typically rely on contrast sensitivity functions (CSFs) of the visual system. Other metrics can be potentially adapted to different viewing distances by rescaling input images. In this paper, we investigate the performance of such adapted metrics together with those that natively account for viewing distance. The results for three testing datasets indicate that there is no evidence that the metrics based on the CSF outperform those that rely on rescaled images. Moreover, we found that both methods are not successful to account for the changes in quality introduced by the change in viewing distance. We conclude that accounting for viewing distances requires better models

Tackling Problems of Marker-Based Augmented Reality Under Water

Underwater sites are a harsh environment for augmented reality applications. Divers must battle poor visibility conditions, difficult navigation, and hard manipulation with devices under water. This chapter focuses on the problem of localizing a device under water using markers. It discusses various filters that enhance and improve underwater images and their impact on marker-based tracking. Then, it presents different combinations of ten image-improving algorithms and four marker-detecting algorithms and tests their performance in real situations. All solutions are designed to run real-time on mobile devices to provide a solid basis for augmented reality. The usability of this solution is evaluated on locations in the Mediterranean Sea. Results show that image improving algorithms with carefully chosen parameters can reduce the problems with underwater visibility and enhance the detection of markers. The best results are obtained with marker detecting algorithms specifically designed for marine environments

DEIMOS – an Open Source Image Database

The DEIMOS (DatabasE of Images: Open Source) is created as an open-source database of images and videos for testing, verification and comparing of various image and/or video processing techniques such as enhancing, compression and reconstruction. The main advantage of DEIMOS is its orientation to various application fields – multimedia, television, security, assistive technology, biomedicine, astronomy etc. The DEIMOS is/will be created gradually step-by-step based upon the contributions of team members. The paper is describing basic parameters of DEIMOS database including application examples