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This presentation outlines a lossless index coding method for indexed colour images aimed at reducing storage costs while preserving image quality. It discusses the process of colour image quantization (CIQ), which involves the design of a representative colour palette, as well as image encoding and decoding. The proposed method enhances CIQ by compressing the index table that stores the indices of colour pixels, using techniques such as Huffman coding. Experimental results demonstrate significant reductions in bit rates without visible degradation, showcasing the method's effectiveness.
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Lossless index coding for indexed colour images Author: Y-C Hu, C-Y Chiang, W-L Chenand W-K Chou Source: Imaging Science Journal, Vol. 60,No.1,pp.54-63,2012 Speaker: Meng-Jing Tsai Date: 2012.03.06
Outline • Introductions • Colour image quantization (CIQ) • Vector quantization (VQ) • The proposed scheme • Experimental results • Conclusion
Introductions • Typically, one RGB colour image consists of three components: red, blue and green, each of which is represented by 1 byte. In other words, 3 bytes are needed to store one colour pixel(像素). • To cut down the storage cost of the RGB colour images, CIQ is thus proposed. • CIQ can be divided into three procedures: palette(調色盤) design, image codingand imagedecoding.
CIQ -Palette Design • The goal of the palette design procedure is to generate a set of representative colour pixels for each RGB colour image. • These colour pixels in turn form the colour palette that will be used in the image encoding/decoding procedures
CIQ - Image Coding • Each colour pixel in the RGB image is to be compressed. • By finding the closestcolour in the palette for each colour pixel, the index of the searched colour is recorded. • The set of indices, also called the index table, is the compressed data of the given RGB image.
CIQ - Image Decoding • The samecolour palettethat was used in the image encoding procedure isneeded to correctly recover the colour image. • By sequentially recoveringeach colour pixel, the whole compressed image canthen be reconstructed.
Vector Quantization (VQ) image vector : x=(x1,x2,…,xk) d(x,y1) d(x,y2) … … d(x,yN) Codebook Original image index of ymin
VQ Encoding Procedure w h Image Index table Index table
VQ Decoding Procedure w h Image Index table Index table
The Proposed Method • To compress the index table of CIQ losslessly, the similarity among neighboring indices in the index table is exploited. • The proposed method consists of the index coding procedure and the index decoding procedure. • The indices are classified into three categories.
Relationship between the CIQ image encoding procedure and the proposed index coding procedure Encoding procedure Decoding procedure
Index Coding Procedure • First categories
Index Coding Procedure • Second category First approach: relatively addressing Second approach: Huffman coding
Index Coding Procedure • Third category • If the index was not founded, stored the original index.
Index Decoding Procedure • By sequentially recoveringeach colour pixel, the whole compressed image canthen be reconstructed.
Conclusion • The proposed method significantly cuts down the number of bit rates of CIQ without incurring any extra image degradation.
