Compression of Cartoon Images

Ty Taylor

ISBN 10: 3659301809 / ISBN 13: 9783659301803

Published by LAP LAMBERT Academic Publishing Nov 2012, 2012

About this Item

Neuware -This book describes a new technique for compressing cartoon images by taking advantage of the distinct color regions in an image and applying a spatially-based compression algorithm. The method for storing these regions involves a combination of Binary Run-Length Encoding and Huffman Encoding. The compression of cartoon images presented here is a lossy compression scheme that removes artifacts and antialiasing before encoding the image, and upon decoding the image, uses an edge-restricted blur filter in an attempt to smooth the edges of the decoded image. Algorithms to support gradient detection, their application, and storage are also described. With these algorithms and the proposed file type, on average the test images were 13.75 times more compact than the corresponding PNG file and 7.45 times more compact than the corresponding JPEG file, with a best bit per pixel ratio of 0.00987 bpp.Books on Demand GmbH, Überseering 33, 22297 Hamburg 84 pp. Englisch.

Seller Inventory # 9783659301803

Bibliographic Details

• Title: Compression of Cartoon Images
• Publisher: LAP LAMBERT Academic Publishing Nov 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 3659301809
• ISBN 13: 9783659301803
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 143 grams

About this title

Synopsis

This book describes a new technique for compressing cartoon images by taking advantage of the distinct color regions in an image and applying a spatially-based compression algorithm. The method for storing these regions involves a combination of Binary Run-Length Encoding and Huffman Encoding. The compression of cartoon images presented here is a lossy compression scheme that removes artifacts and antialiasing before encoding the image, and upon decoding the image, uses an edge-restricted blur filter in an attempt to smooth the edges of the decoded image. Algorithms to support gradient detection, their application, and storage are also described. With these algorithms and the proposed file type, on average the test images were 13.75 times more compact than the corresponding PNG file and 7.45 times more compact than the corresponding JPEG file, with a best bit per pixel ratio of 0.00987 bpp.

About the Author

Ty Taylor graduated from Case Western Reserve University's undergraduate program in Computer Science and from CWRU's graduate program with a specialty in Computer Graphics. Currently, he is a Software Development Engineer at Microsoft.