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Abstract: Problem statement: Efficient color image compression algorithm is essential for mass storage and the transmission of the image. The compression efficiency of the Set Partitioning in Hierarchical Tree (SPIHT) coding algorithm for color images is improved by using correlation theory. Approach: In this study the correlation between the color channels are used to propose the new algorithm. The correlation between the color channels are analyzed in various color spaces and the color space CIE-UVW in which the color channels are highly correlated is taken. The most correlated U channel is considered as base color and compressed by using the wavelet filter and the SPIHT algorithm. The linear approximation of the two of the color components (V and W) based on the primary color component U is used to code subordinate color components. The image is divided into N*N blocks in each color channels. The linear approximation coefficients are calculated for each block of the subordinate colors V and W as functions of the base color. Only these coefficients of each block are coded and send to the receiver along with the SPIHT coding of the base color. Results: By using this algorithm, a significant (4 dB mean value) Peak Signal to Noise Ratio (PSNR) improvement is obtained compared to the traditional coding scheme for the same compression rate and reduces the coding and decoding time. Also the proposed compression algorithm reduces the complexity in coding and decoding algorithms. Conclusion: This algorithm allows the reduction of complexity for both coding and decoding of color images. It is concluded that a significant PSNR gain and visual quality improvement is obtained. It is found that in color image coding, this algorithm is superior to the traditional de-correlation based methods and reduces the coding and decoding time.
Key words: Color image compression, correlation theory, linear approximation, wavelet filter, spiht coding, approximation coefficients, compression algorithms, proposed algorithm, correlation based, luminance component
The uncompressed image data requires a large storage capacity and transmission bandwidth. The purpose of the image compression algorithm is to reduce the amount of data required to represents the image with less degradation in the visual quality and without any information loss (Ghosh and Bhaumik, 2010). In a monochrome image, the neighboring pixels are more correlated. The Discrete Cosine Transform (DCT) and wavelet transform are commonly used to reduce the redundancy between the pixels and for energy compaction. The JPEG standard uses the DCT and the JPEG2000 (Rabbani, 2002) standard uses the wavelet transform. The coefficients of transformation are coded using suitable coding algorithm like Embedded Zero Tree Wavelet (EZW) (Shapiro, 1993) coding or SPIHT (Said, 1996) coding.
In color image, correlation exists between the neighboring pixels of each color channel and as well as between the color channels (San, 2006). In the traditional color image compression algorithm the redundancy between the color channels are reduced by transforming them into a de-correlated color space such as YCbCr, YPbPr, YIQ, YUV. The luminance component contains more details than the chrominance components. To obtain higher compression rate with significant PSNR, the luminance component is compressed at lower rate and the …