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

An Extension of s-CIELAB model for Predicting Colour Difference in Images

In this paper, we propose a knowledge-based taxonomic scheme of the objective image quality assessment metrics including the key concepts involved for each approach. Our classification is constructed according to six criteria based on the information available at each stage of the design process. The novelty of the present classification scheme is that the six layers are linked via a single concept where each layer represents a single type of knowledge about: 1) the reference image, 2) the degradation type, 3) the visual perception field, 4) the human visual physiology and psychophysical mechanisms, 5) the processes of the visual information analysis, and finally 6) knowledge about perceptual image representation and coding. The first layer helps delineate boundaries between full-reference image quality assessment metrics, that are further classified trough layers 2 to 6, and other families (reduced-reference and no-reference). In addition, gradual degrees are considered for knowledge about specific areas related to visual quality evaluation processes. The proposed taxonomic framework is intended to be stepwise, to help sorting out the fundamental ideas behind the development of objective image quality metrics often working on the luminance channel or marginally on the RGB channels. The aim is to congregate the already published classification schemes and to methodologically expand new aspects according to which an efficient and straightforward classification of the image quality assessment algorithms becomes possible. This is significant because of the increasing number of developed metrics. Furthermore, a systematic summarisation is necessary in order to facilitate the search and application of image quality techniques.

Knowledge-based Taxonomic Scheme for Full-Reference Objective Image Quality Measurement Models.

Masking effect is one of the most important perceptual properties that could be modeled by estimating an adaptive threshold known as the just noticeable difference (JND) referring to the maximum difference not perceived by the human visual system (HVS). In this paper, a novel framework to extend 2D-JND models to estimate thresholds for 360-degree images is proposed. The JND is estimated by viewports instead of applying it to the projected format image. Then, the viewport-based JND maps are back-projected to obtain the 360-JND map. To reduce the visible boundaries between viewports an alpha blending process is applied. The validation of the proposed framework is made using subjective experiments. It demonstrates that when applied to 360-degree images, the proposed framework outperforms the 2D-JND models in terms of observers preference at the same noise level.

A Comprehensive Framework for 2D-JND Extension to 360-DEG Images

Recent developments in digital technologies that make use of large scale image and video data, such as the internet, multimedia systems, digital TV, digital cinema, and mobile/wireless devices, have increased the need for efficient processing, storage, and transmission of visual information. It is now widely acknowledged that such technologies can further benefit by incorporating concepts from human vision and corresponding implementation of biologically inspired models. Indeed, in many applications, the human observer plays a crucial role in the decisionmaking tasks such as diagnosis, recognition, and evaluation based on visual assessment of images. Therefore, biologically inspired image/video processing and analysis approaches have been receiving considerable interest in recent years. This special issue presents some of the recent advances in image and video processing techniques inspired by the human visual system (HVS), with applications to multimedia, visual pattern recognition, surveillance, visual information coding and security, and other related subjects. The first paper by Beghdadi, Larabi, Bouzerdoum, and Iftekharuddin provides an overview of the state-of-the-art techniques in perceptual image processing, with particular emphasis on image enhancement, segmentation, and coding. The remaining articles are grouped into four categories, according to the topics addressed in the papers. The first group deals with perceptual video and image coding. The first paper entitled “Perceptual video coding based on MB classification and rate-distortion optimization,” by Guillotel, Aribuki, Olivier and Urban, exploits the perceptual image quality of macroblock in rate-distortion optimization to improve the perceptual quality of coded video sequences. The main idea is to introduce the perceptual distortion metric in the encoding loop in order to adapt the macroblock quantization parameters. A learning strategy is then applied to a number of sequences to provide a set of representative distortion–quantization curves for all macroblock types. The second paper by Hrarti, Saadane and Larabi, entitled “Attentional mechanisms driven adaptive quantization and selective bit allocation scheme for H.264/AVC,” proposes a saliency based

Biologically inspired approaches for visual information processing and analysis

JPEG2000 is an emerging standard for still image compression. It is not only intended to provide rate-distortion and subjective image quality performance superior to existing standards, but also to provide features and additional functionalities that current standards can not address sufficiently such as lossless and lossy compression, progressive transmission by pixel accuracy and by resolution, etc. Currently the JPEG2000 standard is set up for use with the sRGB three-component color space.the aim of this research is to determine thanks to psychovisual experiences whether or not the color space selected will significantly improve the image compression. The RGB, XYZ, CIELAB, CIELUV, YIQ, YCrCb and YUV color spaces were examined and compared. In addition, we started a psychovisual evaluation on the effect of color quantification on JPEG2000 image compression. the final results indicate that the YCrCb color space is very suitable for this standard as mentioned in the amendement2.

Mohamed-Chaker Larabi

Papers

An Extension of s-CIELAB model for Predicting Colour Difference in Images

Knowledge-based Taxonomic Scheme for Full-Reference Objective Image Quality Measurement Models.

A Comprehensive Framework for 2D-JND Extension to 360-DEG Images

Biologically inspired approaches for visual information processing and analysis

Psychovisual evaluation of the effect of color spaces and color quantification in JPEG2000 image compression