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

The latest video coding standard, HEVC, uses quarter pixel motion vector (MV) resolution for motion compensation. The adaptation of MV resolution supported by PMVR (progressive MV resolution) brings further improvement of the performance, by progressively adjusting the resolution according to the distance between the MV and the MV predictor (MVP). In this work, we propose to improve PMVR by adapting MV resolution at the prediction unit (PU) level relying on its size and its average absolute gradient. We additionally perform a smarter motion estimation around multiple MV predictors to fully take advantage of the proposed scheme. Compared to HEVC reference software (HM-16.6), the proposed method provides 1.2%, 3.2% and 1.2% average BD rate savings respectively for random access (RA), low-delay P (LP) and low-delay (LD) configurations.

A block level adaptive MV resolution for video coding

Image super-resolution is a popular technique for increasing the resolution of a given image. Its most common application is to provide better visual effect after resizing a digital image for display or printing. In recent years, due to consumer multimedia products being in vogue, imaging and display device become ubiquitous, and image super-resolution is becoming more and more important. There are mainly three categories of approaches for this problem: interpolation-based methods, reconstruction-based methods, and learning-based methods.

Image Super-Resolution, a State-of-the-Art Review and Evaluation

Rate control is a critical issue in H.264∕AVC video coding standard because it suffers from some shortcomings that make the bit allocation process not optimal. This leads to a video quality that may vary significantly from frame to frame. Our aim is to enhance the rate control efficiency in H.264∕AVC baseline profile by handling two of its defects: the initial quantization parameter (QP) estimation for Intra- Frames (I-Frames) and the target number of bits determination for Inter-Frames (P-Frames) encoding. First, we propose a Rate- Quantization (R-Q) model for the I-Frame constructed empirically after extensive experiments. The optimal initial QP calculation is based on both target bit-rate and I-Frame complexity. The I-Frame target bit-rate is derived from the global target bit-rate by using a new non-linear model. Secondly, we propose an enhancement of the bit allocation process by exploiting frame complexity measures. The target number of bits determination for P-Frames is adjusted by combining two temporal measures: the first is a motion ratio based on actual bits used to encode previous frames; the second measure exploits the difference between two consecutive frames and the histogram of this difference. The simulation results, carried out using the JM15.0 reference software and the JVT-O016 rate control algorithm, show that the right choice of initial QP for I-Frame and first P-Frame allows improvement of both the bit-rate and peak signal-to-noise ratio (PSNR). Finally, the Inter-Frame bit allocation process further improves the bit-rates while keeping the same PSNR improvement (up to .1.33 dB∕ . 2 dB for QCIF/CIF resolutions). Moreover, this process reduces the buffer level variation leading to a more consistent quality of reconstructed videos.

Efficient bit allocation using new intra and inter-frame modeling for H.264/AVC

This study is about 3D scanner conception dedicated to human foot modelling. It is composed by digital devices. The reconstruction method used is based on a stereovision technique. In order to obtain a 3D reconstruction of the patient foot, it is absolutely necessary to extract and reproduce accurately its shape. Consequently, a segmentation stage is unavoidable and requires a specific algorithm given that standard algorithms do not provide enough satisfactory results. Moreover, the environment definition (background color, lighting conditions) depends on the envisaged method. The comparison of the RGB components of several foot skin has shown a weak occurrence of the blue component. A colorimetric study was done using a set of backgrounds with different referenced blue colors (Pantone R ©). The study was done in the HSI color space allowing the decorrelation of the RGB components. A set of images were acquired while combining several foot with different types and different blue backgrounds. So, the study of the hue component obtained shows a perfect uniformity with a sufficiently high saturation component which allows a first segmentation.

Segmentation in the HSI Color Space Dedicated for Medical Imaging.

In this paper, we propose a no-reference perceptual blur metric for 3D stereoscopic images. The proposed approach relies on computing perceptual local blurriness map for each image of the stereo pair. To take into account the disparity/depth masking effect, we modulate the obtained perceptual score at each position of the blurriness maps according to its location in the scene. Under the assumption that, in case of asymmetric stereoscopic image quality, 3D perception mechanisms place more emphasis on the view providing the most important and contrasted information, the two derived local blurriness maps are combined using weighting factors based on local information content. Thanks to the inclusion of those psychophysical findings, the proposed metric handles efficiently symmetric as well as asymmetric distortions. Experimental results show that the proposed metric correlates better with human perception than state-of-the-art metrics.

Mohamed-Chaker Larabi

Papers

A block level adaptive MV resolution for video coding

Image Super-Resolution, a State-of-the-Art Review and Evaluation

Efficient bit allocation using new intra and inter-frame modeling for H.264/AVC

Segmentation in the HSI Color Space Dedicated for Medical Imaging.

No-reference perceptual blur metric for stereoscopic images