Large size images consist of multiple bands data which occupies large space. Image compression becomes important for such large image or data’s to reduce the bandwidth in transmission over a network and in storage space. Wavelet transform is an efficient tool with some limitations for various image processing applications. And these limitations are overcome by complex wavelet transform. In this...

JPEG is an international standard for still image compression [ l ] . The P E G baseline algorithm allows users to supply the custom quantization table and Huffman table to control the compression ratio and the quality of the encoded image. Methods for determining the quantization matrix are usually based on i) ratedistortion theory [2,8] and ii) spatial masking effects of the human visual syst...

Iterative decoding of JPEG images does not perform well due to the poor distance property of the original JPEG Huffman codes. We propose a symmetric RVLC with large free distance which can dramatically improve the system performance when iterative decoding is performed. Simulation results indicate up to 4 dB coding gain is achievable. There is a significant body of literature (see references in...

This paper studies the equivalence problem for cyclic codes of length p and quasi-cyclic codes of length pl. In particular, we generalize the results of Huffman, Job, and Pless (J. Combin. Theory. A, 62, 183–215, 1993), who considered the special case p. This is achieved by explicitly giving the permutations by which two cyclic codes of prime power length are equivalent. This allows us to obtai...

A feature correction two-stage vector quantization (FC2VQ) algorithm was previously developed to compress gray-scale photo identification (ID) pictures. This algorithm is extended to color images in this work. Three options are compared, which apply the FC2VQ algorithm in RGB, YCbCr, and Karhunen-Loeve transform (KLT) color spaces, respectively. The RGB-FC2VQ algorithm is found to yield better ...

The lossless entropy coding used in many image coding schemes often is overlooked as most research is based around the lossy stages of image compression. This paper examines the relative merits of using static Huffman coding with a compact optimal table verses more sophisticated adaptive arithmetic methods. For very low bit rate image compression, the computationally simple Huffman method is sh...

In the standard Huffman coding problem, one is given a set of words and for each word a positive frequency. The goal is to encode each word w as a codeword c(w) over a given alphabet. The encoding must be prefix free (no codeword is a prefix of any other) and should minimize the weighted average codeword size ∑ w freq(w) |c(w)|. The problem has a well-known polynomial-time algorithm due to Huff...

This paper examines the use of arithmetic coding in conjunction with the error resilient entropy code (EREC). The constraints on the coding model are discussed and simulation results are presented and compared to those obtained using Huffman coding. These results show that without the EREC, arithmetic coding is less resilient than Huffman coding, while with the EREC both systems yield comparabl...

Deep Compression is a three stage compression pipeline: pruning, quantization and Huffman coding. Pruning reduces the number of weights by 10x, quantization further improves the compression rate between 27x and 31x. Huffman coding gives more compression: between 35x and 49x. The compression rate already included the metadata for sparse representation. Deep Compression doesn’t incur loss of accu...