Data Compression and Its Application in AI
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 6904
Special Issue Editors
Interests: lossless and lossy data compression algorithm and its application to compressed information processing, e.g., information retrieval, data mining, and machine learning on compressed data
2. JST, PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
Interests: lossless and lossy data compression; IoT systems and applications; computer architecture; embedded system; parallel and distributed system
Special Issue Information
Dear Colleagues,
Research in the past decade has shown that data compression and AI are deeply related to each another. Data compression aims to obtain a succinct representation of redundant data, while AI aims to abstract complex data. Here, it is important to note that algorithms in one field might expand the other. In image/video compression, for example, predicting pixel values is one of the most important tasks that can be improved using deep learning for image recognition. In contrast, deep learning for image recognition can be accelerated by using compressed images as the training data. Such are applications of lossy compression to AI. Moreover, we can also find a close relation between lossless compression and AI, e.g., in language processing. As part of machine translation, pairs of sentences in the target and source languages are given as the training data. In fact, it is known that the translation accuracy can be improved by directly learning from the compressed training data. In this way, data compression and AI are developing while interacting with each other. Addressing this Special Issue, we invite a wide range of theoretical/empirical research on both AI for data compression and compression for AI. We welcome papers including but not limited to the following:
Theoretical research of data compression
Coding methods and expression of compressed information;
Algorithms for making compact data structures;
Analytical study of data compression;
Data compression mechanisms based on machine learning approaches;
Data processing from compressed data structures.
Applications of artificial intelligence with data compression
Lossy and lossless data compression in AI；
Cognitive applications with data compression；
Image and visual applications with data compression；
Human and healthcare applications with data compression；
Medical and biological applications with data compression；
Bigdata processing and data mining with data compression.
Systems with data compression
IoT systems with data compression；
Mobile and ubiquitous computing with data compression；
Processors and accelerators for machine learning with data compression.
Implementation and benchmarks
Software/hardware implementation of machine learning with data compression；
Case studies of LSI and FPGA design for artificial intelligence with data compression；
System-wide usage of data compression in AI applications.
Research survey
Studies from wide areas of scientific research with data compression；
Algorithm surveys of data compression with AI；
Studies of Bigdata processing applying AI with data compression.
Open data and its processing methods by machine learning approaches with data compression
Application of social open data with data compression;
Sensory data and image databases with data compression;
Applications of open data with data compression.
Prof. Dr. Hiroshi Sakamoto
Dr. Shinichi Yamagiwa
Guest Editors
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