PCBMS: A Model to Select an Optimal Lossless Image Compression Technique

Abstract

This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application's requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application.