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

Image Quality and System Performance XI

This paper deals with Computer Aided Diagnosis for skin cancers (melanomas). The diagnosis is based on some rules called the ABCD mnemonics. They take into account color distribution, lesion's diameter, etc. The goal isn't to classify the lesion but to find those, which are the most similar, in order to help the expert to confirm his diagnosis and to avoid any useless excision. This is done thanks to an indexation system, which compare the signatures of previously diagnosed lesions contained in a database and patient's lesion signature. This last is constructed by translating the rules into image processing attributes. We have divided then into three families: Color attributes (color and fuzzy histograms), Texture attributes (co-occurrence matrix and Haralick indices) and Shape attributes (lesion surface and maximum included circle). Image quantization permits us to keep only the most significant colors, thus giving a light structure. Finally, we define a distance for each attribute and use weighted combination for the similarity measure.

Using combination of color, texture, and shape features for image retrieval in melanomas databases

Saliency is one of the most important features in human visual perception. It is widely used nowadays for perceptually optimizing image processing algorithms. Several models have been proposed for 2D images and only few attempts can be observed for 3D ones. In this paper, we propose a stereoscopic 3D saliency model relying on 2D saliency features jointly with depth obtained from monocular cues. On the one hand, the use of 2D saliency features is justified psychophysically by the similarity observed between 2D and 3D attention maps. On the other hand, 3D perception is significantly based on monocular cues. The validation of our model using state-of-the-art procedures including Kullback-Leibler divergence (KLD), area under the curve (AUC) and correlation coefficient (CC) in comparison with attention maps showed very good performance.

Using monocular depth cues for modeling stereoscopic 3D saliency

In Digital Cinema, the video compression must be as transparent as possible to provide the best image quality to the audience The goal of compression is to simplify transport, storing, distribution and projection of films For all those tasks, equipments need to be developed It is thus mandatory to reduce the complexity of the equipments by imposing limitations in the specifications In this sense, the DCI has fixed the maximum bitrate for a compressed stream to 250 Mbps independently from the input format (4K/24fps, 2K/48fps or 2K/24fps) The work described in this paper This parameter is discussed in this paper because it is not consistent to double/quadruple the input rate without increasing the output rate The work presented in this paper is intended to define quantization steps ensuring the visually lossless compression Two steps are followed first to evaluate the effect of each subband separately and then to fin the scaling ratio The obtained results show that it is necessary to increase the bitrate limit for cinema material in order to achieve the visually lossless

HVS-based quantization steps for validation of digital cinema extended bitrates

In this paper, we propose a new perceptually signiﬁcant video quality metric for the H.264/Motion Picture Expert Group (MPEG)-4 Advanced Video Coding (AVC) and MPEG2 standard. Ourmethod operates in the spatial domain by using the Sobel ﬁlter. The proposed approach does notrequire a high computational complexity and can be suitable for real-time evaluation. We evaluatethe performance of the proposed method by using three Common Intermediate Format sequences at dif-ferent compression rates. The comparison of the obtained results is made with some video qualitymodels using “LIVE”, “IVP” and “IRCCyN/IVC 1080i” databases. The performance metrics, i.e. Pearson and Spearman correlation coefficients, indicate that the proposed method gives a good performance inH264 and MPEG2 codec distortions with the three databases comparing with other models.

Mohamed-Chaker Larabi

Papers

Image Quality and System Performance XI

Using combination of color, texture, and shape features for image retrieval in melanomas databases

Using monocular depth cues for modeling stereoscopic 3D saliency

HVS-based quantization steps for validation of digital cinema extended bitrates

A reduced reference video quality assessment of H.264 and mpeg2 codec based on sharpness metric