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Still Image Compression on Parallel Computer Architectures (The Springer International Series in Engineering and Computer Science)
 
 
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Still Image Compression on Parallel Computer Architectures (The Springer International Series in Engineering and Computer Science) [Hardcover]

Savitri Bevinakoppa (Author)

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Book Description

November 30, 1998 0792383222 978-0792383222 1
Still Image Compression on Parallel Computer Architectures investigates the application of parallel-processing techniques to digital image compression. Digital image compression is used to reduce the number of bits required to store an image in computer memory and/or transmit it over a communication link. Over the past decade advancements in technology have spawned many applications of digital imaging, such as photo videotex, desktop publishing, graphics arts, color facsimile, newspaper wire phototransmission and medical imaging. For many other contemporary applications, such as distributed multimedia systems, rapid transmission of images is necessary. Dollar cost as well as time cost of transmission and storage tend to be directly proportional to the volume of data. Therefore, application of digital image compression techniques becomes necessary to minimize costs. A number of digital image compression algorithms have been developed and standardized. With the success of these algorithms, research effort is now directed towards improving implementation techniques. The Joint Photographic Experts Group (JPEG) and Motion Photographic Experts Group(MPEG) are international organizations which have developed digital image compression standards. Hardware (VLSI chips) which implement the JPEG image compression algorithm are available. Such hardware is specific to image compression only and cannot be used for other image processing applications. A flexible means of implementing digital image compression algorithms is still required. An obvious method of processing different imaging applications on general purpose hardware platforms is to develop software implementations. JPEG uses an 8 × 8 block of image samples as the basic element for compression. These blocks are processed sequentially. There is always the possibility of having similar blocks in a given image. If similar blocks in an image are located, then repeated compression of these blocks is not necessary. By locating similar blocks in the image, the speed of compression can be increased and the size of the compressed image can be reduced. Based on this concept an enhancement to the JPEG algorithm is proposed, called Bock Comparator Technique (BCT). Still Image Compression on Parallel Computer Architectures is designed for advanced students and practitioners of computer science. This comprehensive reference provides a foundation for understanding digital image compression techniques and parallel computer architectures.

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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
image partitioning method, scaleup values, pyramid topology, utilisation graph, seventeen processors, digital image compression techniques, least execution time, compressed image size, digital image compression algorithms, speedup graph, compressed image data, image data size, quantisation table, compressed data structure, equal intensity values, three image sizes, block dependency, colour space conversion, efficiency graph, available parallel computers, image data structure, speedup comparison, memory organisation, neighbouring samples, distributed memory architecture
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Image Compression Ratio, Unique Block Number, End Of Image, Local-plus-Global Memory, Reference List, Selection Sort, Non-Inter-Processor Communication, Send Complmg, Similar Block Marker, Speed Improvement Factor, Vector Quantisation, Shared Memory Global, Balanced Workload Image Partitioning, Discrete Cosine Transform, Memory Torus, Similar Block Group, Unique Block Group, Number of Speedup, Number of Efficiency, Number of Execution, Number of Unique Blocks, Block Compression Factor, Distributed Tree, Parameter Symbol Size, Architectu Pyramid
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