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

Considerable research efforts have been devoted to stereo image compression. However, most of them focused on the luminance and ignored the chromatic information. Consequently, in this paper we propose a stereo color image coding method based on colorization. The main idea is to compress one view of the stereo pair using a standard coding method, while for the other view, only the luminance component is considered for compression. The chromatic information of this latter view are transmitted to the decoder for a few representative pixels (RPs) only. These RPs are defined using a novel proposed RP extraction method based on skeletonization. At decoder side, color values of all the remaining pixels are reconstructed by colorization methods. Experimental results showed that our coding method can achieve considerable bit-rate saving compared to conventional coding method.

Adaptive colorization-based compression for stereoscopic images

A large volume of research exists that addresses the mapping, compression, expansion, and other types of manipulation of the extended luminance range afforded by high dynamic range technologies. However, most of this research focuses exclusively on the luminance dimension, ignoring the chromatic information in images. This is particularly problematic given the parallel push toward extending not only the luminance range but also the chromatic range. In this chapter, we look at solutions that combine the ideas of high dynamic range imaging with color management approaches in different contexts.

Color Management in HDR Imaging

This work takes place within the framework of color image retrieval in specialized databases. A CBIR scheme has been proposed allowing to use a description thanks to low-level features in the framework of a high-level concept using a knowledge management. Starting from a representation of the three most frequently used features i.e. color, texture and shape, cooperation techniques are proposed in order to use the expert knowledge in the combination process. Two types of cooperation have been defined; the first is without categorization and the second with categorization. The second approach allows to make a selection of the query category in order to simplify queries in large image databases. Finally, in order to avoid the standard relevance feedback stage, a competition technique has been proposed allowing an unsupervised refinement of the submitted query. The proposed techniques have shown their performances and their robustness and are generalizable to all types of image databases.

Color image retrieval: from low-level representation to high-level concept.

This study examined how the display quality of liquid crystal display (LCD) devices is assessed. A subjective experiment was conducted to identify the factors involved in quality assessments and then to determine their respective contributions to these assessments. The results are expected to drive the development of an objective quality metric dedicated to color reproduction. The subjective data were analyzed by means of a multivariate analysis of variance and a correlation study. The results showed the main effect of display on all the factors that were investigated, namely, hue, contrast, saturation, naturalness, quality, and texture. Finally, among these perceptual features, contrast and hue have been demonstrated to be the most influential on the overall quality of a displayed image.

Empirical Investigation of Display Quality

In this paper, we propose a no-reference (NR) image quality assessment (IQA) metric that operates in the complex frequency domain. A set of features are developed to model the natural scene statistics without depending on any specific visual distortion. The proposed approach relies on a statistical analysis of the transformed image, involving the importance of the phase and magnitude provided by the underlying complex coefficients. We further investigate the correlation between the different image spatial-frequency resolutions, i.e., representations under different scales and orientations in order to extract the directional features and energy distributions of an image. The validation of the NR metric is performed on a variety of challenging IQA databases and the obtained results show good correlation with subjective scores. Besides, the obtained performance is highly competitive compared to the top-performing NR IQA metrics.

Mohamed-Chaker Larabi

Papers

Adaptive colorization-based compression for stereoscopic images

Color Management in HDR Imaging

Color image retrieval: from low-level representation to high-level concept.

Empirical Investigation of Display Quality

Blind image quality assessment in the complex frequency domain