The measurement of meaning

Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

A Survey on Quality of Experience of HTTP Adaptive Streaming

Quality evaluation of underwater images is a key goal of underwater video image retrieval and intelligent processing. To date, no metric has been proposed for underwater color image quality evaluation (UCIQE). The special absorption and scattering characteristics of the water medium do not allow direct application of natural color image quality metrics especially to different underwater environments. In this paper, subjective testing for underwater image quality has been organized. The statistical distribution of the underwater image pixels in the CIELab color space related to subjective evaluation indicates the sharpness and colorful factors correlate well with subjective image quality perception. Based on these, a new UCIQE metric, which is a linear combination of chroma, saturation, and contrast, is proposed to quantify the non-uniform color cast, blurring, and low-contrast that characterize underwater engineering and monitoring images. Experiments are conducted to illustrate the performance of the proposed UCIQE metric and its capability to measure the underwater image enhancement results. They show that the proposed metric has comparable performance to the leading natural color image quality metrics and the underwater grayscale image quality metrics available in the literature, and can predict with higher accuracy the relative amount of degradation with similar image content in underwater environments. Importantly, UCIQE is a simple and fast solution for real-time underwater video processing. The effectiveness of the presented measure is also demonstrated by subjective evaluation. The results show better correlation between the UCIQE and the subjective mean opinion score.

An Underwater Color Image Quality Evaluation Metric

This paper offers a review of present achievements in the field of processing of ceramic-based materials with complex geometry using the main additive manufacturing (AM) technologies In AM, the geometrical design of a desired ceramic-based component is combined with the materials design In this way, the fabrication times and the product costs of ceramic-based parts with required properties can be substantially reduced However, dimensional accuracy and surface finish still remain crucial features in today's AM due to the layer-by-layer formation of the parts In spite of the fact that significant progress has been made in the development of feedstock materials, the most difficult limitations for AM technologies are the restrictions set by material selection for each AM method and aspects considering the inner architectural design of the manufactured parts Hence, any future progress in the field of AM should be based on the improvement of the existing technologies or, alternatively, the development of new approaches with an emphasis on parts allowing the near-net formation of ceramic structures, while optimizing the design of new materials and of the part architecture

Additive Manufacturing of Ceramic‐Based Materials

Quality is a very important parameter for all objects and their functionalities. In image-based object recognition, image quality is a prime criterion. For authentic image quality evaluation, ground truth is required. But in practice, it is very difficult to find the ground truth. Usually, image quality is being assessed by full reference metrics, like MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio). In contrast to MSE and PSNR, recently, two more full reference metrics SSIM (Structured Similarity Indexing Method) and FSIM (Feature Similarity Indexing Method) are developed with a view to compare the structural and feature similarity measures between restored and original objects on the basis of perception. This paper is mainly stressed on comparing different image quality metrics to give a comprehensive view. Experimentation with these metrics using benchmark images is performed through denoising for different noise concentrations. All metrics have given consistent results. However, from representation perspective, SSIM and FSIM are normalized, but MSE and PSNR are not; and from semantic perspective, MSE and PSNR are giving only absolute error; on the other hand, SSIM and PSNR are giving perception and saliency-based error. So, SSIM and FSIM can be treated more understandable than the MSE and PSNR.

/pdf/image-quality-assessment-through-fsim-ssim-mse-and-psnr-a-18245qbzdr.pdf

Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Asymmetric stereoscopic coding is a very promising technique to decrease the bandwidth required for stereoscopic 3D delivery. However, one large obstacle is linked to the limit of asymmetric coding or the just noticeable threshold of asymmetry, so that 3D viewing experience is not altered. By way of subjective experiments, recent works have attempted to identify this asymmetry threshold. However, fixed threshold, highly dependent on the experiment design, do not allow to adapt to quality and content variation of the image. In this paper, we propose a new non-uniform asymmetric stereoscopic coding adjusting in a dynamic manner the level of asymmetry for each image region to ensure unaltered binocular perception. This is achieved by exploiting several HVS-inspired models; specifically we used the Binocular Just Noticeable Difference (BJND) combined with visual saliency map and depth information to quantify precisely the asymmetry threshold. Simulation results show that the proposed method results in up to 44% of bitrate saving and provides better 3D visual quality compared to state-of-the-art asymmetric coding methods.

Asymmetric coding of stereoscopic 3D based on perceptual significance

Durant les dernieres annees, la prise en compte de modeles du Systeme Visuel Humain (SVH) dans l'evaluation de la qualite visuelle des images couleur compressees, est devenu un sujet de recherche majeur. II semble naturel d'integrer davantage les connaissances recentes sur la perception et la modelisation de la vision humaine, dans les systemes de compression d'images. Ainsi, dans cet article, nous proposons une methode d'integration de ces connaissances pour l'augmentation de la qualite visuelle d'images compressees JPEG2000. Cette methode consiste en deux parties: une evaluation de laboratoire pour la modelisation du SVH par la Fonction de Sensibilite au Contraste (CSF) et une technique de calcul de facteurs de ponderation visuelle pour la compression JPEG2000, utilisant le modele SVH evalue, dans le domaine de Fourier de l'image couleur.

Amélioration de la qualité visuelle d'images couleur dans le cadre du standard de compression JPEG2000

The changing of TV systems from 2D to 3D mode is the next expected step in the telecommunication world. Some works have already been done to perform this progress technically, but interaction of the third dimension with humans is not yet clear. Previously, it was found that any increased load of visual system can create visual fatigue, like prolonged TV watching, computer work or video gaming. But watching S3D can cause another nature of visual fatigue, since all S3D technologies creates illusion of the third dimension based on characteristics of binocular vision. In this work we propose to evaluate and compare the visual fatigue from watching 2D and S3D content. This work shows the difference in accumulation of visual fatigue and its assessment for two types of content. In order to perform this comparison eye-tracking experiments using six commercially available movies were conducted. Healthy naive participants took part into the test and gave their answers feeling the subjective evaluation. It was found that watching stereo 3D content induce stronger feeling of visual fatigue than conventional 2D, and the nature of video has an important effect on its increase. Visual characteristics obtained by using eye-tracking were investigated regarding their relation with visual fatigue.

On the comparison of visual discomfort generated by S3D and 2D content based on eye-tracking features

In this paper, we present a no-reference (NR) quality predictor for stereoscopic/3D images based on statistics aggregation of monocular and binocular local contrast features. In particular, for left and right views, we first extract statistical features of the image gradient magnitude (GM) and the Laplacian of Gaussian (LoG), describing the image local structures from different perspectives. The monocular statistical features are then combined to derive the binocular features based on a linear summation model using weightings based on LoGresponse and image local-entropy, independently. These weights can effectively simulate the strength of the views dominance on binocular rivalry (BR) behavior of the human visual system. Subsequently, we further compute the GM features of the difference map between left and right views reflecting the distortion on disparity/depth information. Finally, the BR-inspired combined monocular and disparityrelated binocular features associated with subjective quality scores are jointly used to construct a learned regression model relying on support vector machine regressor. Experimental results on three 3D-IQA benchmark databases demonstrate that our method achieves high quality prediction accuracy and competitive performance compared to state-of-the-art methods.

Blind Stereopair Quality Assessment Using Statistics of Monocular and Binocular Image Structures

This paper deals with image quality assessment. This field plays nowadays an important role in various image
processing applications. Number of objective image quality metrics, that correlate or not, with the subjective
quality have been developed during the last decade. Two categories of metrics can be distinguished, the first with
full-reference and the second with no-reference. Full-reference metric tries to evaluate the distortion introduced
to an image with regards to the reference. No-reference approach attempts to model the judgment of image
quality in a blind way. Unfortunately, the universal image quality model is not on the horizon and empirical
models established on psychophysical experimentation are generally used. In this paper, we focus only on the
second category to evaluate the quality of color reproduction where a blind metric, based on human visual system
modeling is introduced. The objective results are validated by single-media and cross-media subjective tests.

Mohamed-Chaker Larabi

Papers

Asymmetric coding of stereoscopic 3D based on perceptual significance

Amélioration de la qualité visuelle d'images couleur dans le cadre du standard de compression JPEG2000

On the comparison of visual discomfort generated by S3D and 2D content based on eye-tracking features

Blind Stereopair Quality Assessment Using Statistics of Monocular and Binocular Image Structures

Quality assessment for color reproduction using a blind metric