A lifting based system for compression and classification trade off in the JPEG2000 framework

In this paper, we propose a design for a novel lifting based wavelet system that achieves the best trade off between compression and classification performances. 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 compression and/or classification performance of our system in the context of the recent image compression standard, namely JPEG2000. Our simulation results show 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 (at a classification rate of 70%). The generated kernels can also achieve a comparable compression quality with the JPEG2000 kernels whilst providing a 99% classification performance. In other words, the proposed lifting based system achieves the best trade off between compression and classification performance at the compressed bit-stream level in the wavelet domain.