Efficient compression of binary textual images is very important for applications such as document archiving and retrieval, digital libraries and facsimile. The basic property of a textual image is the repetitions of small character images and curves inside the document. Exploiting the redundancy of these repetitions is the key step in most of the coding algorithms. In this paper, we use a simi...

image data require huge amounts of disk space and large bandwidths for transmission. hence, imagecompression is necessary to reduce the amount of data required to represent a digital image. thereforean efficient technique for image compression is highly pushed to demand. although, lots of compressiontechniques are available, but the technique which is faster, memory efficient and simple, surely...

Lossless image coding is an essential requirement for medical imaging applications. Lossless image compression techniques usually have two major components: adaptive prediction and adaptive entropy coding. This paper is concerned with adaptive prediction. Recently, several researchers have studied prediction schemes in which the final prediction is formed by a combination of a group of sub-pred...

3D dynamic meshes are associated with voluminous data and need to be encoded for efficient storage and transmission. We study the impact of vertex clustering on registration-based dynamic mesh coding, where compact mesh motion representation is achieved by computing correspondences for the mesh segments from the temporal reference to obtain high compression performance. Clustering algorithms se...

Applications in scientific computing operate with high-volume numerical data and the occupied space should be reduced. Traditional compression algorithms cannot provide sufficient compression ratio for such kind of data. We propose a lossless algorithm of delta-compression (a variant of predictive coding) that packs the higher-order differences between adjacent data elements. The algorithm take...

The most important features of the developed image database system include compressed domain indexing, searching by using scalable features, and progressive image transmission. User interaction is involved at both the search re nement stage and display of the query results. The indexing and searching algorithms are tightly coupled with the underlying image compression algorithm by means the ima...

Many classes of images contain some spatial regions which are more important than other regions. Compression methods which are capable of delivering higher reconstruction quality for the important parts are attractive in this situation. For medical images, only a small portion of the image might be diagnostically useful, but the cost of a wrong interpretation is high. Algorithms which deliver l...

Distributed source coding makes it possible to develop compression algorithms with a low−complexity encoder, while most of the signal modeling is moved to the decoder. This structure is an excellent match to the remote sensing scenario, in which the on−board processing units have limited computational capabilities. Moreover, remote sensing images do not exhibit motion, which greatly simplifies ...

Many classes of images contain some spatial regions which are more important than other regions. Compression methods which are capable of delivering higher reconstruction quality for the important parts are attractive in this situation. For medical images, only a small portion of the image might be diagnostically useful, but the cost of a wrong interpretation is high. Algorithms which deliver l...

A new data structure, namely “cumulative frequency matrix (CFM)”, is proposed here for maintaining cumulative frequencies. For an order-0 model having 256 symbols, CFM is a 2-D array of 16 rows and 16 columns. Two nibbles, say L for left and R for right, of a byte symbol represents row and column dimensions respectively. Matrix element (L, R) represents cumulative frequency of symbol with right...