Matthew G. Reyes

Lossy Compression of Bilevel Images Based on Markov Random Fields

A new method for lossy compression of bilevel images based on Markov random fields (MRFs) is proposed. It preserves key structural information about the image, and then reconstructs the smoothest image that is consistent with this information. The smoother the original image, the lower the required bit rate, and conversely, the lower the bit rate, the smoother the approximation provided by the decoded image. The main idea is that as long as the key structural information is preserved, then any smooth contours consistent with this information will provide an acceptable reconstructed image. The use of MRFs in the decoding stage is the key to efficient compression. Experimental results demonstrate that the new technique outperforms existing lossy compression techniques, and provides substantially lower rates than lossless techniques (JBIG) with little loss in perceived image quality.

Lossless Reduced Cutset Coding of Markov Random Fields

This paper presents Reduced Cutset Coding, a new Arithmetic Coding (AC) based approach tolossless compression of Markov random fields. In recent work\cite{reye:09a}, the authors presented an efficient AC based approachto encoding acyclic MRFs and described a Local Conditioning (LC)based approach to encoding cyclic MRFs. In the present work, weintroduce an algorithm for AC encoding of a cyclic MRF for which thecomplexity of the LC method of \cite{reye:09a}, or the acyclicMRF algorithm of \cite{reye:09a} combined with the Junction Tree(JT) algorithm, is too large. For encoding an MRF based on acyclic graph

Lossy Cutset Coding of Bilevel Images Based on Markov Random Fields

G=(V,E)

Arithmetic encoding of Markov random fields

, a cutset

Structure-preserving properties of bilevel image compression

U\subset V

Entropy bounds for a Markov random subfield

is selected such thatthe subgraph

Minimum conditional description length estimation for Markov random fields

G_U

Covariance and entropy in Markov random fields

induced by

Cutset width and spacing for Reduced Cutset Coding of Markov random fields

U

Row-centric lossless compression of Markov images

, and each of the components of