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

Rate control algorithm adopted in H.264/AVC reference software shows several shortcomings that have been highlighted by different studies. For instance, in the baseline profile, the frame target bit-rate estimation assumes similar characteristics for all frames and the quantization parameter determination uses the Mean Absolute Difference for complexity estimation. Consequently, an inefficient bit allocation is performed leading to important quality variation of decoded sequences. A saliency-based rate-control is proposed in this paper to achieve bit-rate saving and improve perceived quality. The saliency map of each frame, simulating the human visual attention by a bottom-up approach, is used at the frame level to adjust the quantization parameter and at the macroblock level to guide the bit allocation process. Simulation results show that the proposed attentional model is well correlated to human behavior. When compared to JM15.0 reference software, at the frame level, the saliency map exploitation achieves bit-rate savings of up to 26%. At the MB level and under the same quality constraint, bit-rate improvement is up to 42% and buffer level variation is reduced by up to 71%.

Attentional mechanisms driven adaptive quantization and selective bit allocation scheme for H.264/AVC

No-reference (NR) stereoscopic 3D (S3D) image quality assessment (SIQA) is still challenging due to the poor understanding of how the human visual system (HVS) judges image quality based on binocular vision In this paper, we propose an efficient opinion-aware NR Stereoscopic Quality predictor based on local contrast statistics combination (SQSC) Specifically, for left and right views, we first extract statistical features of the gradient magnitude (GM) and Laplacian of Gaussian (LoG) responses, describing the image local structures from different perspectives The HVS is insensitive to low-order statistical redundancies that can be removed by LoG filtering Hence, the monocular statistical features are then fused to derive the binocular features based on a linear combination model using LoG responses-based weightings These weightings can efficiently simulate the binocular rivalry (BR) phenomenon Finally, the binocular features and the subjective scores were jointly employed to construct a learned regression model obtained by the support vector regression (SVR) algorithm Experimental results on three widely used 3D IQA databases demonstrate the high prediction performance of the proposed method when compared to recent well performing SIQA methods

No-Reference Quality Assessment of Stereoscopic Images Based on Binocular Combination of Local Features Statistics

The 3D High Efficiency Video Coding (3D-HEVC) is the latest 3D extension of the HEVC video coding standard. It supports multi-view videos plus depth (MVD), which is a sophisticated format for enhanced 3D content. Depth modelling modes (DMM) are adopted in the 3D-HEVC for better sharp edge encoding. However, employing DMM causes important computational complexity. In this paper, we propose a new method for fast wedgelet pattern decision in 3D-HEVC. In order to avoid unnecessary evaluation of the DMMs, a new early termination based on the smoothness of the current block is
adopted. Besides, based on the shuffled frog leaping algorithm, a new heuristic method to search the optimal wedgelet pattern for DMM1 Depth mode is introduced. The proposed algorithm outperforms the HTM16.0 implemented scheme in terms of encoding time with similar coding efficiency.

Heuristic inspired search method for fast wedgelet pattern decision

Codage et analyse d'images couleur : application à l'indexation de bases d'images réparties

In this work, a foveal wavelet-based Mean Shift Tracking Algorithm is presented. The foveal wavelets introduced by Mallat [16] are known by their high capability to precisely characterize the holder regularity of singularities. Therefore, by using the foveal wavelet transform, image features are accurately identified and are well discriminated from noise. These wavelets are used to extract the texture features of the target object. The extracted features are then used to construct a joint color-foveal textures histogram to represent the target object. Once the joint histogram is obtained, it is applied to the mean shift framework in order to track a target object in a video sequence. The experimental results showed that the proposed approach overcomes the traditional mean shift tracking technique as well as other existing tracking algorithms.

Mohamed-Chaker Larabi

Papers

Attentional mechanisms driven adaptive quantization and selective bit allocation scheme for H.264/AVC

No-Reference Quality Assessment of Stereoscopic Images Based on Binocular Combination of Local Features Statistics

Heuristic inspired search method for fast wedgelet pattern decision

Codage et analyse d'images couleur : application à l'indexation de bases d'images réparties

Robust foveal wavelet-based object tracking