Journal Article

Improved Recurrence Plots Compression Distance by Learning Parameter for Video Compression Quality

Murai, Tatsumasa, Koga, Hisashi

2023

Overview

As the Internet-of-Things is deployed widely, many time-series data are generated everyday. Thus, classifying time-series automatically has become important. Compression-based pattern recognition has attracted attention, because it can analyze various data universally with few model parameters. RPCD (Recurrent Plots Compression Distance) is known as a compression-based time-series classification method. First, RPCD transforms time-series data into an image called “Recurrent Plots (RP)”. Then, the distance between two time-series data is determined as the dissimilarity between their RPs. Here, the dissimilarity between two images is computed from the file size, when an MPEG-1 encoder compresses the video, which serializes the two images in order. In this paper, by analyzing the RPCD, we give an important insight that the quality parameter for the MPEG-1 encoding that controls the resolution of compressed videos influences the classification performance very much. We also show that the optimal parameter value depends extremely on the dataset to be classified: Interestingly, the optimal value for one dataset can make the RPCD fall behind a naive random classifier for another dataset. Supported by these insights, we propose an improved version of RPCD named qRPCD, which searches the optimal parameter value by means of cross-validation. Experimentally, qRPCD works superiorly to the original RPCD by about 4% in terms of classification accuracy.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Classification / Coders / compression-based pattern recognition / Data compression / Datasets / Internet of Things / Methods / MPEG encoders / MPEG-1 / Parameter estimation / Parameters / Pattern recognition / Quality management / recurrence plots / Time series / time series classification / Time-series analysis / Video compression