Improving mesh-based motion compensation by using edge adaptive graph-based compensated wavelet lifting for medical data sets

TL;DR

This paper introduces an edge adaptive graph-based motion compensation method for mesh-based wavelet lifting in medical imaging, significantly enhancing visual quality without increasing data size.

Contribution

It proposes a novel use of distorted edge lengths for motion compensation, improving wavelet transform quality in medical data processing.

Findings

Improves lowpass band quality by approximately 2.5 dB.

Maintains the same filesize for motion information.

Enhances visual quality of medical images.

Abstract

Medical applications like Computed Tomography (CT) or Magnetic Resonance Tomography (MRT) often require an efficient scalable representation of their huge output volumes in the further processing chain of medical routine. A downscaled version of such a signal can be obtained by using image and video coders based on wavelet transforms. The visual quality of the resulting lowpass band, which shall be used as a representative, can be improved by applying motion compensation methods during the transform. This paper presents a new approach of using the distorted edge lengths of a mesh-based compensated grid instead of the approximated intensity values of the underlying frame to perform a motion compensation. We will show that an edge adaptive graph-based compensation and its usage for compensated wavelet lifting improves the visual quality of the lowpass band by approximately 2.5 dB compared to the traditional mesh-based compensation, while the additional filesize required for coding the motion information doesn't change.