Bit allocation algorithm for wavelet image compression

We introduce a compression algorithm using the wavelet transform. The principle of wavelet transform is to decompose hierarchically the input image into a series of successively lower resolution reference images and detail images that contain the information needed to be reconstructed back to the next higher resolution level. The histogram of image sub-bands provides information on the distribution of the coefficient values in this sub image. The sub band images resulting from wavelet transform are not equal significance. Some sub-bands contain more information than others. The total number of available bits describe an image is however, inevitably limited. Therefore, it is desirable to allocate more bits to those sub-band images and can be coded more accurately than others. The objective of such bit allocation method is to optimize the overall coder performance and minimize the quantization error. In determining which wavelet filter is to be used for image compression, some of the properties including vanishing moments are considered. The wavelet transform is implemented using a linear-phase biorthogonal filter (16,4) with 4 level of decomposition. For this study, we used a scalar quantization with uniform threshold quantizers. The quantization method is pulse code modulation (PCM) for the coefficients in all high-pass sub-bands. The coefficients of low-pass sub-band are differential PCM quantized per region.