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- Cao, Bin, et al.
(författare)
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Multiobjective Image Compression based on Tensor Decomposition
- 2023
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Ingår i: 2023 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA. - : IEEE. - 9781665455336 - 9781665455343 ; , s. 545-550
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Konferensbidrag (refereegranskat)abstract
- Most of the traditional image compression methods vectorize the data contained in the image and then compress it. However, this approach does not take into account the high-dimensional information inside the image. To solve this problem, this paper regards the color image as a third-order tensor, and proposes a method of color image compression based on Tucker decomposition and multiobjective optimization. The tensor size compression ratio and Hu invariant moment similarity are proposed to measure the image compression quality. And to more comprehensively consider the sensitivity of human visual system to different visual signals, the five-objective optimization model of image compression is constructed. The five-objective optimization model includes: the above two indexes, information content weighted structure similarity index, color image feature similarity and information fidelity criterion. In addition, an angle-aware opposition-based learning strategy is proposed to improve the reference vector guided selection strategy of RVEA*. In the experiments, this method could effectively solve the problem of color image compression.
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| 2. |
- Song, William Wei, Professor, 1960-
(författare)
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An Approximation Computation Approach to Big Data Analysis with a Case Analysis of PV System
- 2023
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Ingår i: 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665455336 ; , s. 44-52
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Konferensbidrag (refereegranskat)abstract
- In the era of big data, it is indispensable to apply data science and technology for big data analysis to solve the big data problems. With the advancement of the big data technologies, we are also facing many problems when dealing with the big data and their studies. It is obvious that big data become "bigger"and "bigger", more complex than before, with a good number of attributes and features in various formats and styles. On the other hand, many data analysis techniques have been proposed for various application domain problems in different purposes. This worsens the situation of choosing an appropriate method for a right problem of right data. In this paper, the author intends to propose an approximation approach toward this problem, through discussing the ways of identification of patterns of the original data, be they of data features or analysis methods. The author attempts to apply the idea to a case of fault detection of a household photovoltaic system. © 2023 IEEE.
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