Abstract
Compression is a technique to reduce the quantity of data without excessively reducing the quality of the multimedia data.The transition and storing of compressed multimedia data is much faster and more efficient than original uncompressed multimedia data. There are various techniques and standards for multimedia data compression, especially for image compression such as the JPEG and JPEG2000 standards. These standards consist of different functions such as color space conversion and entropy coding. Arithmetic and Huffman coding are normally used in the entropy coding phase. In this paper we try to answer the following question. Which entropy coding, arithmetic or Huffman, is more suitable compared to other from the compression ratio, performance, and implementation points of view? We have implemented and tested Huffman and arithmetic algorithms. Our implemented results show that compression ratio of arithmetic coding is better than Huffman coding, while the performance of the Huffman coding is higher than arithmetic coding. In addition, implementation of Huffman coding is much easier than the arithmetic coding.
Highlights
Multimedia data, especially images have been increasing every day
Since the human visual system is less sensitive to the position and motion of color than luminance [6, 7].some color space conversions such as RGB to YCbCr are used [29, 8].The step of the JPEG standard consists of Discrete Cosine Transform (DCT)
In order to determine which entropy coding is suitable from performance, compression ratio, and implementation points of view, we focus on the mentioned algorithms in this paper
Summary
Multimedia data, especially images have been increasing every day. Because of their large capacity, storing and transmitting are not easy and they need large storage devices and high bandwidth network systems. Huffman and arithmetic coding are the two most important entropy coding in image compression standards. Arithmetic or Huffman, is more suitable from the compression ratio, performance, and implementation points of view compared to other? We have studied, implemented, and tested these important algorithms using different image contents and sizes.
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