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.

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

In this paper, we propose a new, perceptually significant video quality metric to estimate the blocking effect caused by the H.264/MPEG-4 Advanced Video Coding (AVC) standard. Our method operates in the spatial domain by taking into account the local variation inside each 16 by 16 block. The proposed approach does not require high computational complexity and can be suitable for real-time evaluation. We evaluate the performance of the proposed method by using three common intermediate format sequences at different compression rates. The comparison of the obtained results was made with Suthaharan's technique using the IVP database. The performance metrics, Pearson and Spearman correlation coefficients, indicate that the proposed method outperformed the approach recommended in the literature for block-based compression, and the improvement was even better for the H.264/MPEG-4 AVC standard.

A Perceptual Measure of Blocking Artifact for No-reference Video Quality Evaluation of H.264 Codec

Stereoscopic imaging technologies are seen as the next generation of visual presentation, improving the quality of experience of the viewer. It uses two different sequences acquired from two regular cameras or from a regular camera with an additional specific depth camera. This means that the size of data is at least doubled. Thus, the coding process becomes very crucial. In this framework, we propose a stereoscopic coder based on visual properties. The matching of two images is computed by a binocular energy model based on the simple and complex cells functions allowing the fusion of both retinal images in the visual cortex. Mathematical functions were used to reproduce the behavior of these cells particularly complex wavelet transform (CWT) and bandelet transform. Our coder output is a disparity map, a residual image and the reference image. The innovative part of this work lies in a matching technique based on the binocular energy. The results are presented in comparative curves with one of the most known coder in literature.

Stereo image coding based on binocular energy modeling

Models of the human visual system are particularly interesting to quantify the quality of the display systems and to predict if visual information will be perceptible or not. One of these models is the contrast sensitivity function (CSF) which characterizes the sensitivity of the visual system to the spatial and temporal frequencies. The achromatic CSF can be measured, relatively, by a method of pairing which consists in matching the contrast of a test grid with that of a reference grid. To determine the reproducible grids on a screen, it is practical to use a frequency/observation distance diagram. The tests of this study are carried out under the conditions of medical diagnosis for radiographies with sinusoidal stimuli. The obtained results were approximated by a model in order to facilitate their integration in other models.

A Novel Approach for Constructing an Achromatic Contrast Sensitivity Function by Matching

Quality assessment is becoming an important issue in the framework of image processing. This need is expressed by the fact that the quality threshold of end-users has been shifted up because of the large availability of high fidelity sensors at very affordable price. This observation has been made for different application domains such as printing, compression, transmission, and so on. Starting from this, it becomes very important to manufacturers and producers to provide products of high quality to attract the consumer. This high interest on quality means that tools to measure it have to be available. This work is dedicated to the comparison of subjective methodologies in the digital cinema framework. The main goal is to determine with a group of observers, which methodology is better for assessing digital cinema content and what is the annoyance level associated to each of them. Several configurations are tested side by side, Butterfly, one by one, Horizontal scroll, vertical scroll, Horizontal and vertical scroll.

Comparison of subjective assessment protocols for digital cinema applications

In this investigation we identify relationships between human and automated face recognition systems with respect to compression. Further, we identify the most influential scene parameters on the performance of each recognition system. The work includes testing of the systems with compressed Closed-Circuit Television (CCTV) footage, consisting of quantified scene (footage) parameters. Parameters describe the content of scenes concerning camera to subject distance, facial angle, scene brightness, and spatio-temporal busyness. These parameters have been previously shown to affect the human visibility of useful facial information, but not much work has been carried out to assess the influence they have on automated recognition systems. In this investigation, the methodology previously employed in the human investigation is adopted, to assess performance of three different automated systems: Principal Component Analysis, Linear Discriminant Analysis, and Kernel Fisher Analysis. Results show that the automated systems are more tolerant to compression than humans. In automated systems, mixed brightness scenes were the most affected and low brightness scenes were the least affected by compression. In contrast for humans, low brightness scenes were the most affected and medium brightness scenes the least affected. Findings have the potential to broaden the methods used for testing imaging systems for security applications.

Mohamed-Chaker Larabi

Papers

A Perceptual Measure of Blocking Artifact for No-reference Video Quality Evaluation of H.264 Codec

Stereo image coding based on binocular energy modeling

A Novel Approach for Constructing an Achromatic Contrast Sensitivity Function by Matching

Comparison of subjective assessment protocols for digital cinema applications

Comparative performance between human and automated face recognition systems, using CCTV imagery, different compression levels and scene parameters