Lossy/lossless coding of bi-level images

Abstract

 Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Rate-Distortion Optimized Graph-Based Representation for Multiview Images With Complex Camera Configurations

“…• First pixel location: The exact location of the first pixels of each contour is described by a bright point in a bi-level image, as shown in Fig.7.c. This bi-level image is then encoded by JBIG (Joint Bi-level Image Experts Group) [16]; • Direction stream {∆x}: The exact locations of other pixels (except first pixels) of C n (p) are encoded by the arithmetic edge coding (AEC) method [17] (the direction stream in Fig.7.c); • d (u) stream {d (u) (x, y)}: Once the locations of C n (p) have been encoded, the d (u) values on C n (p) have also to be losslessly encoded from top to bottom (the d (u) stream in Fig.7.c). Note that we finally use a Differential Pulse Code Modulation (DPCM) coder to encode the d (u) stream, i.e.…”

“…The constructed graph edges are finally represented by the unidimensional disparity maps. To code the unidimensional disparity maps, we first propose a lossless compression scheme using the mix of JBIG [16] and arithmetic edge coding [17] for the position of non-zero values in unidimensional disparity maps and DPCM for the unidimensional disparity values. A lossy compression with HEVC of the unidimensional disparity maps has also been considered to further reduce the bitrate.…”

Lossless Compression of Color Map Images by Context Tree Modeling

“…The combination of these pixel values is called context. The probabilities of the pixels, generated under the given context, are usually treated as being independent [19]. In 2-D modeling, the context is defined by the set of closest pixels.…”

JBIG2-the ultimate bi-level image coding standard

“…JBIG2 allows lossy compression due to preprocessing of the original image, which may consist of flyspeck noise removal and text filtering to still be visually lossless [3], or even complex halftone image processing algorithms based on rate-distortion theory [4]. BIG2 also permits the lossy compression that results from pattern matching and substitution in the encoder.…”

“…Compression of this type of image data is also addressed by existing facsimile standards, such as ITU-T Recommendations T. 4 …”