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

An audiovisual saliency model for conferencing and conversation videos

Study of visual fatigue/discomfort generated by S3D video using eye-tracking data

Saliency models have been extensively studied for static images and the focus is now on moving images. There is a central bias in both cases that is emphasized in the dynamic case. One aspect in this latter is the camera motion that influences the scene interpretation. The movie director exploits this motion to make the observer focus on the targeted object which is often in the center of the scene. This aspect is not taken into account in current saliency dynamic models. In this paper, we study the camera motion influence on the gaze distribution in order to include it in a new saliency model. Observers' gazes are recorded with an eye tracker, camera motions (e.g. tracking, zoom…) are calculated thanks to a polynomial projection of the motion field and the motion influence is statistically tested on the recorded gazes.

/pdf/camera-motion-influence-on-dynamic-saliency-central-bias-4kkkx7e2xt.pdf

Camera motion influence on dynamic saliency central bias

The physical validation of computer-generated images (CGIs) has received a lot of attention from the computer
graphics community, as opposed to the assessment of these images' psychovisual quality. The field indeed lacks
the proper tools to quantify the perceptual quality of a CGI. This paper engages in the construction of such a
metric. A psychovisual experiment was submitted to a representative panel of observers, where participants were
asked to score the overall quality and aspects of this quality for several CGIs. An analytical model was fit to
the data, giving insight into the relative perceptual importances of these aspects. Accuracy in the simulation of
shadows, good contrast and absence of noise were found to have a major impact on the perceived quality, rather
than precise anti-aliasing and faithfull color bleeding.

/pdf/towards-a-perceptual-quality-metric-for-computer-generated-4yb5gfxmvu.pdf

Towards a perceptual quality metric for computer-generated images

One of the most challenging issues in stereoscopic image quality assessment (SIQA) is how to effectively model the binocular behavior of the human visual system (HVS). The latter has a great impact on the perceptual 3D quality. In this paper, we propose a SIQA metric accounting for binocular combination properties and disparity information. Instead of computing the quality of the left and the right views separately, the proposed metric predicts the quality of a cyclopean image so as to have a good consistency with 3D human perception. The cyclopean image is synthesized based on the local entropy and the visual saliency of each view with the aim to simulate the phenomena of binocular fusion/rivalry. A 2D IQA metric is employed to assess the quality of both the cyclopean image and the disparity map. The obtained scores are used to derive the 3D quality score thanks to a pooling stage. Experimental results on three public 3D IQA databases show that the proposed method outperforms many other state-of-the-art SIQA methods, and achieves high prediction accuracy on these databases.

Mohamed-Chaker Larabi

Papers

An audiovisual saliency model for conferencing and conversation videos

Study of visual fatigue/discomfort generated by S3D video using eye-tracking data

Camera motion influence on dynamic saliency central bias

Towards a perceptual quality metric for computer-generated images

Full-reference stereoscopic image quality assessment accounting for binocular combination and disparity information