Optimizing Rate-Distortion Performance of Motion Compensated Wavelet Lifting with Denoised Prediction and Update

TL;DR

This paper introduces a method to optimize denoising filter strength within motion compensated wavelet lifting for medical data, improving lossless compression efficiency while maintaining perfect reconstruction.

Contribution

It proposes a novel rate-distortion based approach to select optimal denoising filter strength in wavelet lifting for medical sequences.

Findings

Significant reduction in rate for medical sequences.

Effective control of distortion via a single parameter.

Preservation of temporal base layer similarity.

Abstract

Efficient lossless coding of medical volume data with temporal axis can be achieved by motion compensated wavelet lifting. As side benefit, a scalable bit stream is generated, which allows for displaying the data at different resolution layers, highly demanded for telemedicine applications. Additionally, the similarity of the temporal base layer to the input sequence is preserved by the use of motion compensated temporal filtering. However, for medical sequences the overall rate is increased due to the specific noise characteristics of the data. The use of denoising filters inside the lifting structure can improve the compression efficiency significantly without endangering the property of perfect reconstruction. However, the design of an optimum filter is a crucial task. In this paper, we present a new method for selecting the optimal filter strength for a certain denoising filter in a rate-distortion sense. This allows to minimize the required rate based on a single input parameter for the encoder to control the requested distortion of the temporal base layer.