Quality of experience (QoE) that serves as a direct evaluation of viewing experience from the end users is of vital importance for network optimization, and should be constantly monitored. Unlike existing video-on-demand streaming services, real-time interactivity is critical to the mobile live broadcasting experience for both broadcasters and their audiences. While existing QoE metrics that are validated on limited video contents and synthetic stall patterns have shown effectiveness in their trained QoE benchmarks, a common caveat is that they often encounter challenges in practical live broadcasting scenarios, where one needs to accurately understand the activity in the video with fluctuating QoE and figure out what is going to happen to support the real-time feedback to the broadcaster. In this paper, we propose a temporal relational reasoning guided QoE evaluation approach for mobile live video broadcasting, namely TRR-QoE, which explicitly attends to the temporal relationships between consecutive frames to achieve a more comprehensive understanding of the distortion-aware variation. In our design, video frames are first processed by deep neural network (DNN) to extract quality-indicative features. Afterwards, besides explicitly integrating features of individual frames to account for the spatial distortion information, multi-scale temporal relational information corresponding to diverse temporal resolutions are made full use of to capture temporal-distortion-aware variation. As a result, the overall QoE prediction could be derived by combining both aspects. The results of experiments conducted on a number of benchmark databases demonstrate the superiority of TRR-QoE over the representative state-of-the-art metrics.
Image set coding improves the compression efficiency by reducing both intra- and inter-image redundancy. The key of success is to select representative image(s) to predict set of similar images. This paper proposes an inter-image redundancy measure for representative image selection in image set compression. In the proposed method, the inter-image redundancy is measured jointly by the extent of similar content (EOS) and the correlation of similar content (COS) shared in two images. We take the covered area of matched SIFT points to measure the EOS, and take the distance of the matched SIFT descriptors to measure the COS. The image with largest redundancy for the set is selected as the representative one to predict other images. Experimental results show that the proposed method can select better representative image, and achieve bitrate saving up to 9.2% and 20.8% compared with state-of-the-art image set compression method and HEVC inter coding method.
To solve the fault-tolerant and migration problems of simulation grid applications, the art of related research works is introduced firstly. Then, by adopting reflective software analysis and modeling method, the independent and dynamic fault-tolerant and migration model for simulation resources is proposed. Further more, the research fruits on several related key technologies are presented in detail, which include: to inspect the resources, the loading model is defined and the independent error forecast is achieved; the consistency of the simulation time is ensured by the harmonious advance of the state management of federates and the distributed simulation time management; and the automatic storage and restoring of state is accomplished. The research fruits above have been applied to the development of fault-tolerant and migration service for COSIM-CSP1.0v, and gained well validation in some typical applications. The multidisciplinary, distributed and collaborative simulation application for undercarriage virtual prototype is introduced as an example. Finally the conclusion is given.
Blind video quality assessment (BVQA) plays a pivotal role in evaluating and improving the viewing experience of end-users across a wide range of video-based platforms and services. Contemporary deep learning-based models primarily analyze the video content in its aggressively downsampled format, while being blind to the impact of actual spatial resolution and frame rate on video quality. In this paper, we propose a modular BVQA model, and a method of training it to improve its modularity. Specifically, our model comprises a base quality predictor, a spatial rectifier, and a temporal rectifier, responding to the visual content and distortion, spatial resolution, and frame rate changes on video quality, respectively. During training, spatial and temporal rectifiers are dropped out with some probabilities so as to make the base quality predictor a standalone BVQA model, which should work better with the rectifiers. Extensive experiments on both professionally-generated content and user generated content video databases show that our quality model achieves superior or comparable performance to current methods. Furthermore, the modularity of our model offers a great opportunity to analyze existing video quality databases in terms of their spatial and temporal complexities. Last, our BVQA model is cost-effective to add other quality-relevant video attributes such as dynamic range and color gamut as additional rectifiers.
In this paper, we propose an effective no-reference image quality assessment (IQA) method based on local region statistics (NRLRS). The proposed method is built on the hypothesis that image distortions may alter the local region statistics which can be well characterized by the inter-pixel relationship. Hence, by extracting perceptual features that describe the inter-pixel patterns of a distorted image, we can effectively quantify the impact of image degradation. For this purpose, the perceptual gray-level differences between neighboring pixels are extracted and a Gaussian Mixture Model (GMM) codebook is constructed as the generative model of extracted features. The Fisher vector representation is then derived to describe image as their derivations from the GMM model. Finally, partial least square regression is used to map the Fisher encodings to quality scores. Experimental results indicate that the proposed method achieves better performance in quality prediction as compared to relevant full-reference and no-reference IQA methods.
The article proceeds from the educational fair theory.Through its advocacy by the principles of freedom and equality,the principle of equal opportunities,the ability difference principle,the principle of compensation for the disadvantaged,the principle of giving priority to the efficiency of the five basic principles of discourse and interpretation.Analyses of the distance education system for public service in object of service,business framework,undertake the responsibility,welfare strategy,and the running mode one by one.In conclusion,the article pointed out that the distance education support services to the public education system is the ideal of fair carrier,is the benefit of the public at large.As long as it exists,people rich or poor,noble or lowly,smart or stupid,the pursuit of a dream of higher education will not burst.
During the past few years, there have been various kinds of content-aware image retargeting operators proposed for image resizing. However, the lack of effective objective retargeting quality assessment metrics limits the further development of image retargeting techniques. Different from traditional image quality assessment (IQA) metrics, the quality degradation during image retargeting is caused by artificial retargeting modifications, and the difficulty for image retargeting quality assessment (IRQA) lies in the alternation of the image resolution and content, which makes it impossible to directly evaluate the quality degradation like traditional IQA. In this paper, we interpret the image retargeting in a unified framework of resampling grid generation and forward resampling. We show that the geometric change estimation is an efficient way to clarify the relationship between the images. We formulate the geometric change estimation as a backward registration problem with Markov random field and provide an effective solution. The geometric change aims to provide the evidence about how the original image is resized into the target image. Under the guidance of the geometric change, we develop a novel aspect ratio similarity (ARS) metric to evaluate the visual quality of retargeted images by exploiting the local block changes with a visual importance pooling strategy. Experimental results on the publicly available MIT RetargetMe and CUHK data sets demonstrate that the proposed ARS can predict more accurate visual quality of retargeted images compared with the state-of-the-art IRQA metrics.
Image set compression has recently emerged as an active research topic due to the rapidly increasing demand in cloud storage. In this paper, we propose a novel framework for image set compression based on the rate-distortion optimized sparse coding. Specifically, given a set of similar images, one representative image is first identified according to the similarity among these images, and a dictionary can be learned subsequently in wavelet domain from the training samples collected from the representative image. In order to improve coding efficiency, the dictionary atoms are reordered according to their use frequencies when representing the representative image. As such, the remaining images can be efficiently compressed with sparse coding based on the reordered dictionary that is highly adaptive to the content of the image set. To further improve the efficiency of sparse coding, the number of dictionary atoms for image patches is further optimized in a rate-distortion sense. Experimental results show that the proposed method can significantly improve the image compression performance compared with JPEG, JPEG2000, and the state-of-the-art dictionary learning-based methods.
This paper attempts to explore and unscramble the theory of media in distance education thoroughly and systematically through choosing principles and choosing method, which could make the choice of media, the most interesting but difficult thing in distance teaching and learning, become relatively easy for both teachers and learners.
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