DEVELOPMENT OF AN ALGORITHM FOR WAVELET TRANSFORM IN A JPEG LIKE IMAGE CODER

Abstract

Image compression is now essential for applications such as transmission of large sized images and their storage in databases. The fundamental goal of image compression is to reduce the number of bits for transmission or storage while maintaining an acceptable image quality. Compression can be achieved by transforming the data, projecting it on a basis function, and encoding this transform. A new Discrete Wavelet Transform (DWT) based image compression algorithm is presented in this thesis whose structure is modeled on the lines of the "Baseline JPEG (Joint Picture Expert Group) standard". The JPEG algorithm that uses the Discrete Cosine Transform (DCT) yields good results for compression ratios till 10-15:1. As the compression ratio increases, the coarse quantization on the image blocks causes blocking artifacts in the decompressed image which is very annoying to the Human Visual System (HVS). The wavelet transform that has better decorrleating properties than the DCT operates on the complete image and hence it does not create any blocking artifacts and provides significant improvement in achieving compression. Lifting scheme which is an easy, efficient and the latest advancement in the field of implementing conventional wavelets by the use of simple steps has been used to implement the wavelet transform and the multiresolution decomposition of the images at different scales. This is followed by the quantization of the transformed coefficients. The transformed coefficients which are now at different resolution levels are quantized using block thresholding. The block thresholding levels are fixed such that the coefficients in the lowest resolution range are least quantized whereas the coefficients in the higher resolution range are quantized more severely. The final 3 blocks which contain the highest frequency components are completely quantized to a zero-level mark. Finally entropy encoding (Run length encoding and Huffman encoding) is done to achieve final compression. This algorithm produces compression results competitive with the DCT base Baseline JPEG model and requires no training, no pre-stored tables or codebooks and requires no prior knowledge of the image source. Fill Results