Adaptive wavelet filters in image coders-how important are they?

Wavelet-based image coding algorithms lossy or lossless, use a fixed perfect reconstruction (PR) filterbank built into the algorithm for coding and decoding all kinds of images. This generic approach of filter selection and usage may not always give the best compression from the viewpoint of a specific application. However, no systematic study has been done to see if using different wavelet filters for different image types improves the coding performance. To explore this problem, a variety of wavelets are used to compress a variety of images at different compression ratios and the results are reported here. The result, intuitive at best, is that the performance in lossy coders is image dependent and while some wavelet filters perform better than others depending on the image being coded, no specific wavelet filter performs uniformly better than others on all test images. This observation leads to the hypothesis that both for lossy and lossless compression, the “most appropriate” wavelets should be chosen adaptively depending on the statistical nature of image being coded, to achieve better compression