Quality assessment plays a crucial role in data analysis. In this paper, we present a reduced-reference approach to volume data quality assessment. Our algorithm extracts important statistical information from the original data in the wavelet domain. Using the extracted information as feature and predefined distance functions, we are able to identify and quantify the quality loss in the reduced or distorted version of data, eliminating the need to access the original data. Our feature representation is naturally organized in the form of multiple scales, which facilitates quality evaluation of data with different resolutions. The feature can be effectively compressed in size. We have experimented with our algorithm on scientific and medical data sets of various sizes and characteristics. Our results show that the size of the feature does not increase in proportion to the size of original data. This ensures the scalability of our algorithm and makes it very applicable for quality assessment of large-scale data sets. Additionally, the feature could be used to repair the reduced or distorted data for quality improvement. Finally, our approach can be treated as a new way to evaluate the uncertainty introduced by different versions of data.

Abstract — This paper presents a novel theoretical framework for end-to-end video quality prediction of MPEG-based video sequences. The proposed framework encloses two discrete models: i) A model for predicting the video quality of an encoded signal at a pre-encoding stage and ii) A model for mapping QoSsensitive network parameters (i.e. packet loss) to video quality degradation. The efficiency of both the discrete models is experimentally validated, proving by this way the accuracy of the proposed framework. Keywords-Video Quality; Packet Loss;MPEG;H.264 I.

Abstract. The growing power of parallel supercomputers gives scientists the ability to simulate more complex problems at higher fidelity, leading to many high-impact scientific advances. To maximize the utilization of the vast amount of data generated by these simulations, scientists also need scalable solutions for studying their data to different extents and at different abstraction levels. As we move into peta- and exa-scale computing, simply dumping as much raw simulation data as the storage capacity allows for post-processing analysis and visualization is no longer a viable approach. A common practice is to use a separate parallel computer to prepare data for subsequent analysis and visualization. A naive realization of this strategy not only limits the amount of data that can be saved, but also turns I/O into a performance bottleneck when using a large parallel system. We conjecture that the most plausible solution for the peta- and exa-scale data problem is to reduce or transform the data in-situ as it is being generated, so the amount of data that must be transferred over the network is kept to a minimum. In this paper, we discuss different approaches to in-situ processing and visualization as well as the results of our preliminary study using large-scale simulation codes on massively parallel supercomputers. 1.

We describe a method for removing quantization artifacts (de-quantizing) in the image domain, by enforcing a high degree of sparseness in its representation with an overcomplete oriented pyramid. For this purpose we devise a linear operator that returns the minimum L2-norm image preserving a set of significant coefficients, and estimate the original by minimizing the cardinality of that subset, always ensuring that the result is compatible with the quantized observation. We implement this solution by alternated projections onto convex sets, and test it through simulations with a set of standard images. Results are highly satisfactory in terms of performance, robustness and efficiency.

Low-quality videos often not only have limited resolution, but also suffer from noise. Directly up-sampling a video without considering noise could deteriorate its visual quality due to magnifying noise. This paper addresses this problem with a unified framework that achieves simultaneous de-noising and super-resolution. This framework formulates noisy video super-resolution as an optimization problem, aiming to maximize the visual quality of the result. We consider a good quality result to be fidelity-preserving, detailpreserving and smooth. Accordingly, we propose measures for these qualities in the scenario of de-noising and superresolution. The experiments on a variety of noisy videos demonstrate the effectiveness of the presented algorithm.

Traditionally, image quality assessment has involved the comparison of a corrupted image with an “original ” or perfect version of that given image. In many practical settings, this perfect image is not available. This research introduces a new metric that measures the perceived visual quality of a single given image. Operating in this no-reference framework, the new method is ideally suited for real-world applications, including television monitoring and digital camera quality sensing. Much of the theoretical basis of this work centers on the notion of level-of-detail. Knowing whether an image is highly detailed or very smooth is important in both the detection and assessment of errors. At this time, three types of errors that commonly arise in practice are considered, namely noise, blur and compression. Each given image is assigned a score reflecting its perceived quality. Human test cases validate the new techniques.