عنوان البحث(Papers / Research Title)
DCT Coefficients Compression Using Embedded Zerotree
الناشر \ المحرر \ الكاتب (Author / Editor / Publisher)
توفيق عبد الخالق عباس الاسدي
Citation Information
توفيق,عبد,الخالق,عباس,الاسدي ,DCT Coefficients Compression Using Embedded Zerotree , Time 5/10/2011 6:32:37 PM : كلية تكنولوجيا المعلومات
وصف الابستركت (Abstract)
DCT Coefficients Compression Using Embedded ZerotreeDCT Coefficients Compression Using Embedded ZerotreeDCT Coefficients Compression Using Embedded ZerotreeDCT Coefficients Compression Using Embedded Zerotree
الوصف الكامل (Full Abstract)
DCT Coefficients Compression Using Embedded Zerotree Algorithm
Dr. Tawfiq A. Abbas and Asa ad N. Hashim
Abstract:
The goal of compression algorithms is to gain best compression ratio withacceptable visual quality, the proposed compression satisfy this goal. EmbeddedZerotree Algorithm works efficiently because of the following hypothesis:- " If aDCT coefficient C at a coarse scale is insignificant with respect to a giventhreshold T, i.e. |C|<T then all DCT coefficients of the same orientation at finerscales are also likely to be insignificant with respect to T".
Introduction
Image compression is possible because images in general, are highlycoherent(nonrandom),which means that there is redundant information. Visual datalike other meaningful data, are usually structured, and this structure means that dataover different parts of an image are interrelated. For example, consider an image inmatrix format, if we take an arbitrary pixel, its color will likely be close to that ofneighboring pixels, since they are more likely than not to belong to the same object. Inany case, there are usually some redundant data because of the image structure. Imagecompression methods try to eliminate some of this redundancy to produce a morecompact code that preserves the essential information contained in the image[2].Main question about compression algorithms is how does one judge the qualityof one versus another. In the case of lossless compression there are several criteriasuch that the time to compress, the time to reconstruct, the size of the compressedfiles, in the case of lossy compression the judgment is further complicated since wealso have to worry about how good the lossy approximation is. There are typicallytradeoffs between the amount of compression, the runtime, and the quality of thereconstruction. Depending on your application one might be more important thananother and one would want to pick your algorithm appropriately[3].
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