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.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Matthew G. Reyes, Xiaonan Zhao, David L. Neuhoff, Thrasyvoulos N. Pappas
• Keywords: Bit Rate, ICIP 2007, Image Processing, Key Structural Information, Lossy Compression