Colour Image Compression With Colour Conversion and Hybrid Algorithm (original) (raw)

The astounding augmentation of multimedia in the fields of communication media, medicine, surveillance etc. resulted in the huge volume of data acquirement. The storage of these data requires massive memory. For communication, these data need enormous transmission bandwidth. The only solution to reduce the storage and the transmission bandwidth is the data compression. From the literature survey it is learnt that there is a need to achieve compression ratio greater than 30 with a PSNR greater than 25 dB for non critical applications. In order to facilitate this, a colour image compression method is proposed. In this method, the colour image is converted into the "YCbCr" format using formulated New Equation Set-1. The "Y" component matrix is divided into 16×16 blocks. The DCT is applied to all the 16×16 blocks. The DC-Coefficient of all 16×16 block DCT is taken out and zero is inserted in place of it. The data types of all the DC-coefficients are changed from the "double" to the "16 bit integer" data type and they are stored. The transformed matrix consists of 16×16 block DCT of all the blocks. In this matrix, all those elements less than the threshold value "th" are made zero. This matrix is decomposed into matrices "U", "S" and "V" using SVD. All those elements of the matrix "U" less than the threshold value "thu" , all those elements of the matrix "S" less than the threshold value "ths" and all those elements of the matrix "V" less than the threshold value "thv" are made zero. Then these matrices are multiplied to form one matrix such that X=USV T. All those elements of the matrix "X" less than the threshold value "th" are made zero. Now all the elements of the matrix "X" are divided by 10. Then the matrix "X" becomes a sparse matrix. This sparse matrix is represented in the "triplet form". The data types of the "row values" and the "column values" of the triplet form are converted from the "double" to the "16 bit integer" data type. The data type of the "data elements" of the "triplet form" is converted into the "8 bit integer" data type. Then the RLE is applied to the "column values" of the "triplet form". After this, the compressed form of the Y-Component Matrix is obtained. Similarly, the "Cb" and the "Cr" component matrices are compressed. Then the experiments are conducted by converting the given image into the "YCbCr" format by the formulated New Equation Set-2, New Equation Set-3 and the basic "YCbCr" equation. The results are compared with parameters such as Compression Ratio, PSNR, SSIM and Quality Index. Experiments are conducted using MATLAB. From the results, it can be concluded that, the compression ratio obtained from the method which has got the colour conversion using New Equation Set-1 is good. The maximum compression ratio obtained with this method is 43.5079 with a PSNR of Red, Green and Blue Component equal to 25.9583 dB, 25.7501 dB and 26.4837 dB respectively. I. LITERATURE SURVEY There are different contributions to the above discussed problem. Few papers are discussed in this section. Raghevendra.M.J and others [1][2] have worked on image compression using DCT and SVD. Raghavendra.M.J and others [3] have worked on image compression using combinations of DCT, SVD and RLE. In this work, it is possible to achieve a compression ratio of 34.2325 with a PSNR of 25.2174 dB for a grayscale image. Raghavendra M.J and others [4] have worked on colour image compression. The paper [4] is in press. In this [4] Block wise operation is not done and the colour conversion operation is not done. In [4] the maximum compression ratio obtained is 32.1552 with a PSNR of around 24dB. Prasanta.H.S and others have worked on image compression using SVD [5]. In this a compression ratio of 4.12 with a PSNR of 43.85dB is obtained for the 32-Rank of the S-Matrix of the SVD. In this paper, an insight is given such that as we decrease the rank of the S-matrix of the SVD, the compression ratio increases. S.R.Subramanya and others have worked on wavelet transform [6] with predictive coding. Chandan .S.R and others have worked on compression of images using DCT and Fractal encoding [7]. Anna.