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 problem of determining the best level of asymmetry has been addressed by several recent works with the aim to guarantee an optimal binocular perception while keeping the minimum required information. To do so, subjective experiments have been conducted for the definition of an appropriate threshold. However, such an approach is lacking in terms of generalization because of the content variability. Moreover, using a fixed threshold does not allow an adaptation to the content and to the images' quality. The traditional asymmetric stereoscopic coding methods apply a uniform asymmetry by considering that all regions of an image have the same perceptual relevance which is not in compliance with the characteristics of human visual system (HVS). Consequently, this paper describes a fully automated model that dynamically determines the best bounds of asymmetry for each region of the image. Based on the Binocular Just Noticeable Difference (BJND) and the depth level in the scene, the proposed method achieves non-uniform reduction of spatial resolution of one view of the stereo pair with the aim to reduce bandwidth requirement. Experimental results show that the proposed method results in up to 43% of bitrate saving while outperforming the widely used asymmetric coding approaches in terms of 3D visual quality.

Asymmetric coding using Binocular Just Noticeable Difference and depth information for stereoscopic 3D

Media content retargeting aims to adapt images/ videos to displays of large or small sizes. In this work, we propose a bandelet-based image retargeting algorithm for summarizing image data into smaller sizes. First, we define a multi-scale bandelet-based perceptual similarity measure which measures the geometric and perceptual similarities between two images at different bandelet scales. Two images are said to be geometrically similar if they have approximately the same geometric flow and quadtree structure. After determining the geometric similarity, a perceptual similarity measure based on the properties of the human visual system is defined to assess the perceptual difference between the original image and the retargeted one. Then, the problem of image retargeting is considered as a geometric optimization problem based on the bandelet-based geometric and perceptual similarity measures. That is, for an image S we search for a retargeted image T that contains as much as possible of geometric and perceptual information from S and, consequently, preserves visual coherence. The proposed retargeting algorithm outperforms the state-of-the-art methods in terms of the visual quality of the retargeted image.

Image retargeting using a bandelet-based similarity measure

Visual Quality Enhancement for Colour Images in the Framework of the JPEG2000 Compression Standard

A new Image Quality Metric (IQM) dedicated to image in-painting evaluation is proposed in this paper. This objective measure is computed by combining some structural and salient features related to the specific goals of inpainting purposes. The main perceptual characteristics used in this approach are the visual coherence and the visual saliency. The proposed Inpainted Image Quality Assessment is evaluated trough some experimental results. It is shown that the proposed metric is consistent with the perceptual evaluation of image inpainting results.

Inpainted image quality assessment

Video encoding rate control has been the research focus in the recent years. The existing rate control algorithms use Rate-Distortion (R-D) or Rate-Quantization (R-Q) models. These latter assume that the enhancement of the bit allocation process, the quantization parameter determination and the buffer management are essentially based on the improvement of complexity measures estimation. Inaccurate estimation leads to wrong quantization parameters and affects significantly the global performance. Therefore, several improved frame complexity measures are proposed in literature. The efficiency of such measures is however limited by the linear prediction model which remains still inaccurate to encode complexity between two neighbour frames. In this paper, we propose a new approach of Rate-Quantization modeling for both Intra and Inter frame without any complexity measure estimation. This approach results from extensive experiments and proposes two Rate-Quantization models. The first one (M1) aims at determining an optimal initial quantization parameter for Intra frames based on sequence target bit-rate and frame rate. The second model (M2) determines the quantization parameter of Inter coding unit (Frame or Macroblock) according to the statistics of the previous coded ones. This model substitutes both linear and quadratic models used in H.264 rate controller. The simulations have been carried out using both JM10.2 and JM15.0 reference softwares. Compared to JM10.2, M1 alone, improves the PSNR up to 1.93dB, M2 achieves a closer output bit-rate and similar quality while the combined model (M1+M2) minimizes the computational complexity. (M1+M2) outperforms both JM10.2 and JM15.0 in terms of PSNR.

Mohamed-Chaker Larabi

Papers

Asymmetric coding using Binocular Just Noticeable Difference and depth information for stereoscopic 3D

Image retargeting using a bandelet-based similarity measure

Visual Quality Enhancement for Colour Images in the Framework of the JPEG2000 Compression Standard

Inpainted image quality assessment

A New approach of Rate-Quantization modeling for Intra and Inter frames in H.264 rate control