Accurate Blind Lempel-Ziv-77 Parameter Estimation via 1-D to 2-D Data Conversion Over Convolutional Neural Network

Abstract

The data sequence compressed using the Lempel-Ziv-77 (LZ77) algorithm comprises a series of fixed-length tuples. To decompress this data, it is essential to know the encoding parameters such as the tuple length used for the compression. If this essential information becomes unavailable owing to conditions such as loss of the header, it is difficult to accurately determine the parameters using the compressed data. In this study, we investigate the blind estimation of tuple length from the LZ77-compressed data when the header is unavailable. To this end, we propose a novel idea of utilizing the LZ77 image generated from the LZ77-compressed data. The LZ77 image exhibits unique patterns based on the image size. The correlation between the image size and tuple length is indicated by different patterns of vertical lines in the LZ77 image. By exploiting a convolutional neural network (CNN), we develop an iterative algorithm while generating LZ77 images with different size. The results of the experiment on a public database show that the LZ77 image plays the role of an extremely powerful visual feature descriptor, and the proposed iterative algorithm estimates the tuple length with 100% accuracy.

Keywords

• Convolutional neural network
• deep learning
• Lempel-ziv-77
• machine learning
• parameter estimation