Spectral Video Compression Using Convolutional Sparse Coding
Spectral Videos (SV) are datasets containing spatial spectral and temporal information of a moving scene and this kind of information have been successfully used in medicine, remote sensing, and military application. However, expensive acquisition processes and difficulties in equipment manufacture lead to low resolution datasets. Therefore, super resolution (SR) techniques have emerged as a processing tool that recovers a high resolution dataset by expressing the measurements as compressed versions of the desired data. Furthermore, the Convolutional Sparse Coding (CSC) has been developed as a signal model that learns a dictionary directly from the target signal, improving the reconstruction quality. This work proposes to extend the CSC formulation to consider temporal correlations in SVs, exploiting the shifting invariance property of the CSC model. The simulation results show a PSNR improvement in up to 2.5dB with respect to the state of the art methods, preserving the edges and textures of the spectral video frames.
2020 Data Compression Conference (DCC)