This work investigates the development and application of orthonormal motion-adaptive transforms for efficient image sequence processing. The author, Du Liu, presents novel transform techniques designed to adapt to the motion within image sequences, aiming to improve compression and analysis. The research focuses on the mathematical underpinnings of these transforms and their practical implementation in computer vision and signal processing applications. The objective is to provide a more effective framework for handling the spatio-temporal redundancies inherent in video data.

The research presented in this work likely targets a specialized academic audience within the fields of image processing and computer vision. The focus on orthonormal motion-adaptive transforms suggests a technical and mathematically rigorous approach, aiming to advance the state-of-the-art in video analysis and compression. The work is expected to contribute to the ongoing development of efficient algorithms for handling dynamic visual data. Its reception would likely be within academic circles and among researchers in related engineering disciplines.