Lossy Image Compression: Domain Decomposition-Based by K.K. Shukla, M.V. Prasad (auth.)

By K.K. Shukla, M.V. Prasad (auth.)

Good caliber electronic photos have excessive garage and bandwidth requisites. nowa days, with expanding consumer expectation for photograph caliber, effective compression is critical to maintain reminiscence and transmission time inside of moderate limits.

Image compression is anxious with minimization of the variety of details wearing devices used to symbolize a picture. Lossy compression strategies incur a few lack of details that's often imperceptible. In go back for accepting this distortion, we receive a lot larger compression ratios than is feasible with lossless compression.

Salient positive factors of this ebook include:

  • Four new picture compression algorithms and implementation of those algorithms
  • Detailed dialogue of fuzzy geometry measures and their program in photo compression algorithms
  • New area decomposition dependent algorithms utilizing snapshot caliber measures and research of assorted caliber measures for grey scale photograph compression
  • Compression algorithms for various parallel architectures and assessment of time complexity for encoding on all architectures
  • Parallel implementation of photo compression algorithms on a cluster in Parallel digital laptop (PVM) environment.

This booklet could be of curiosity to graduate scholars, researchers and working towards engineers searching for new picture compression recommendations that supply sturdy perceived caliber in electronic pictures with larger compression ratios than is feasible with traditional algorithms.

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Additional resources for Lossy Image Compression: Domain Decomposition-Based Algorithms, 1st Edition

Sample text

1 for some integer k C 1. The domain decomposition triangles need not be rightangled isosceles, but their shapes are determined by the distribution of gray values in the image. By placing the new vertex at the point of maximum error, we get less number of decompositions (lower average depth of recursion), or for the same number of decomposition the quality of the reconstructed image is significantly improved. The resulting tree structure is shown in Fig. 6; and is stored in a binary string S obtained from the breath-first traversal of the tree.

5a if Err (x, y) exceeds e. The process is repeated indefinitely, we eventually obtain triangles comprising only three adjacent pixels, which are the vertices surely satisfying Eq. 8) since inequalities in Eq. 6) ensure that Err (x, y) = 0 at each of these vertices. The relevant topological information is stored in a hierarchical structure—a tree, a node represents each triangle whose position in the tree implicitly defines the vertex coordinates. 5b shows how the partition process works: each time a triangle is subdivided the resulting triangles becomes its children in the tree.

C. Else decompose the triangle into six new triangles by first joining the point of maximum error with the three vertices of the original triangle and then by joining the point of maximum error with the midpoints of the opposite sides of the three new triangles. d. Push the six new triangles on the stack. Repeat step 3 for all the remaining triangles in the stack. Advantage of this algorithm is the number of thin triangles formed was greatly reduced and there was improvement in the quality of images.

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