Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/18534
Title: Reduction of artifacts and edge preservation of images
Researcher: Sivakami Sundari K
Guide(s): Sadasivam V
Keywords: Computer Science
Total blocking Error
Genetic Algorithm
Upload Date: 22-May-2014
University: Manonmaniam Sundaranar University
Completed Date: December, 2007
Abstract: Image and video applications require compressed images with high compression ratio to overcome the difficulties in dealing huge volume of image data. The requirement is in general in conjunction with the desire for high quality of the resulting content. The qualities of the transmitted images are decreased due to artifacts created during compression. At high compression ratios, the error introduced by quantisation of the transform coefficients produce visually undesirable patterns known as compression artifacts that dramatically lower the perceived quality of a particular image. The tradeoff between compression ratio and image quality of all compression scheme can be compromised by the reduction of artifacts. A great deal of effort has been invested in attempts to solve this problem while preserving the information content of the image. Blocking artifacts of JPEG images and ringing artifacts of JPEG 2000 images plays crucial role in many applications. Preprocessing, Transform Domain processing, and Post processing are the existing artifact reduction techniques and attention is diverted to optimize the solution with any one of these approaches. newlineCurrent work computes the measure of artifacts with the new metric named as Total blocking Error (TBE). Minimization of TBE is an indication about the elimination of the artifacts. Proposed work primarily concentrates on the blocking artifacts of JPEG images and to a small amount of concentration over the ringing artifacts of JPEG 2000 images. New algorithm has been implemented in Transform domain with a modified Quantisation table and filter. Efficient suppression of artifacts can be controlled by the scaling parameter in the Quantisation process, and by the kernel in the filtering process. Both the process are adaptive and to be optimized. From the literary survey it is found that Genetic Algorithm (GA), has not been used so far for the optimization of the reduction of artifacts.
Pagination: v,166p.
URI: http://hdl.handle.net/10603/18534
Appears in Departments:Department of Computer Science & Engg.

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01_title.pdfAttached File53.59 kBAdobe PDFView/Open
02_certificate.pdf49.85 kBAdobe PDFView/Open
03_acknowledgement.pdf58.19 kBAdobe PDFView/Open
04_contents.pdf78.3 kBAdobe PDFView/Open
05_list of tables.pdf68.05 kBAdobe PDFView/Open
06_chapter 1.pdf98.5 kBAdobe PDFView/Open
07_chapter 2.pdf209.88 kBAdobe PDFView/Open
08_chapter 3.pdf235.32 kBAdobe PDFView/Open
09_chapter 4.pdf2.03 MBAdobe PDFView/Open
10_chapter 5.pdf3.72 MBAdobe PDFView/Open
11_chapter 6.pdf264.76 kBAdobe PDFView/Open
12_references.pdf180.61 kBAdobe PDFView/Open


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