In this paper, we propose a novel design for a lifting based wavelet system that achieves the optimal trade off between compression and classification performances. In addition, it can also achieve a superior compression performance compared to existing wavelet kernels. The proposed system is based on bi-orthogonal filters and can operate in a scalable compression framework. In the proposed system, the trade off point between compression and
classification is determined by the system, however the user can also fine-tune the relative performance using two controllers (one for compression and one for classification). Extensive simulations have been performed to demonstrate the superior compression and/or classification performance of our system in the context of the recent image compression standard, namely (JPEG2000). Our simulation result shows that the lifting based kernels, generated from the proposed system, are capable of achieving superior compression performance compared to the default kernels adopted in the JPEG2000 standard (with a classification rate of 70%). The generated kernels can also achieve a comparable compression quality with the JPEG2000 kernels whilst also provide a 99% classification rate. In other words the proposed lifting based system achieves the trade off between compression and classification performance in the wavelet domain.