A complexity analysis of the JPEG image compression algorithm

Abstract

Digital Image Processing (DIP) is one of the fastest evolving fields in Computer Science with growing numbers of applications. As the demand for multimedia increases, more advanced sensors are being developed, producing a more demanding volume of data. As a result, the theory of data compression has become extremely significant in reducing data redundancy to save hardware storage space and transmission bandwidth in the context of digital imagery. In this paper, we will analyze the complexities of the various algorithms implemented in JPEG Image Compression Algorithm both theoretically and experimentally. These algorithms include Discrete Cosine Transform, Quantization, and Symbol Encoding techniques such as Huffman and Arithmetic Coding. Finally, we will explore different optimization schemes in terms of computational performance.