Low-Rate Entropy Coding Of Transformed Images

Abstract

TWo entropy coding schemes are investigated in this paper by estimating the entropies that specify the lower bounds of their coding rates. In the firSt scheme, we use a traditional combination of runlength and Huffman codes. Arithmetic codes are used in the second scheme. The results indicate that binary arithmetic codes outperform runlength codes by a factor of 34 % for low-rate coding of the zero-valued coefficients of the cosine transform of digital images. Hexadecimal truncated arithmetic codes provided a coding rate improvement as high as 28 % over truncated Huffman codes at low rates. The complexity of these arithmetic codes is suitable for practical implementation.