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.

/pdf/image-quality-assessment-through-fsim-ssim-mse-and-psnr-a-18245qbzdr.pdf

Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study

Image inpainting is not only the art of restoring damaged images but also a powerful technique for image editing e.g. removing undesired objects, recomposing images, etc. Recently, it becomes an active research topic in image processing because of its challenging aspect and extensive use in various real-world applications. In this paper, we propose a novel efficient approach for high-quality and fast image restoration by combining a greedy strategy and a global optimization strategy based on a pyramidal representation of the image. The proposed approach is validated on different state-of-the-art images. Moreover, a comparative validation shows that the proposed approach outperforms the literature in addition to a very low complexity.

/pdf/a-hierarchical-approach-for-high-quality-and-fast-image-39udvg2un3.pdf

A Hierarchical Approach for High-Quality and Fast Image Completion

3D applications need a stereo matching algorithm that can merge the left image with the right image. In this paper we propose a stereo matching algorithm based on some binocular properties of the human visual system. For this, we have focused on the simple and complex cells properties that are responsible for binocular fusion. A model, based on a complex wavelet transform, a color contrast sensitivity function and a bandelet transform, is proposed to model the properties of the simple and complex cells characterized by their size, orientation, phase and amplitude. This model is designed to calculate the binocular energy generated by each binocular complex cell. A model refinement based on properties specific to the lateral geniculate body is then proposed to refine the results obtained with the binocular energy model calculation. The obtained results can be exploited by the stereoscopic vision applications, such as 3D reconstruction, stereoscopic coding, stereoscopic quality assessment and other applications.

Towards acomprehensive color stereoscopic matching model based on HVS behavior

Exploration of comfort factors for virtual reality environments

This work lies within the scope of content-based image retrieval and browsing. Our interest is related on the one hand, to the extraction of features in the compressed domain with the JPEG2000 standard and on the other hand, to the multispectral images acquired within the framework of project PIMHAI (European project INTERREG III B ATLANTIC AREA) dedicated to the monitoring of the environment of the Atlantic littoral. Multispectral images are characterized by a high number of bands and an important resolution. In order to extract the relevant content from the images and then to build the signatures which will represent them, we jointly used features calculated in the spatial domain and others in the compressed domain. These latter features are applied to all the sub-bands of the wavelet transform (DWT) except for low frequency sub-band (named LL). This one is represented using features from the spatial domain. The similarity measurement between images is calculated using the fusionof thefeatures carriedout by themeans ofweighting factors resulting from a preliminary training. The results are posted on a browsing interface, which is installed in order to simplify browsing and retrieval.

http://ieeexplore.ieee.org/iel5/4275036/4275037/04275074.pdf

Usingspatialandcompressedfeaturesformulti-spectralimage retrieval

In this work, a wavelet-based coding scheme for stereoscopic images is presented. The proposed scheme effectively integrates the coding of the disparity map in addition to the reference image. Hence, it first calculates the wavelet transform of the left and right images. A process for detecting an optimal scale for the Discrete Wavelet Transform (DWT) is then applied. The sub-bands of the last retained level of the DWT are then segmented into quadtree. The disparity map is obtained by studying the similarity between the dyadic squares belonging to the left image and those of the right image. This is achieved by minimizing an energy function defined on the spatial properties of the dyadic squares. Thus, the binocular compensation/suppression process allows at this stage to take advantage of the gap existing generally between the two views. Finally, the coefficients of the reference image, the residual image and the disparity map are encoded using a quantization step followed by an entropic coding. Of course, the target image is predicted using the aforementioned content. The experimental results of the proposed approach shows a good performance compared to the literature.

Mohamed-Chaker Larabi

Papers

A Hierarchical Approach for High-Quality and Fast Image Completion

Towards acomprehensive color stereoscopic matching model based on HVS behavior

Exploration of comfort factors for virtual reality environments

Usingspatialandcompressedfeaturesformulti-spectralimage retrieval

Stereo image coding based on the binocular compensation/suppression