Distributed Video Coding (DVC) is a new video coding paradigm which mainly exploits the source statistics at the decoder based on the availability of some decoder side information. The quality of the side information has a major impact on the DVC rate-distortion (RD) performance in the same way the quality of the predictions had a major impact in predictive video coding. In this paper, a DVC solution exploiting multiple side information is proposed; the multiple side information is generated by frame interpolation and frame extrapolation targeting to improve the side information of a single estimation mode. Compared with the best available single side information solutions, the proposed DVC solution with multiple side information robustly improves the RD performance for the set of test sequences.

Transform Domain Wyner-Ziv (TDWZ) video coding is an efficient Distributed Video coding solution providing new features such as low complexity encoding, by mainly exploiting the source statistics at the decoder based on the availability of decoder side information. The accuracy of the decoder side information has a major impact on the performance of TDWZ. In this paper, a novel multi-hypothesis based TDWZ video coding is presented to exploit the redundancy between multiple side information and the source information. The decoder used optical flow for side information calculation. Compared with the best available single estimation mode TDWZ, the proposed multi-hypothesis based TDWZ achieves robustly better Rate-Distortion (RD) performance and the overall improvement is up to 0.6 dB at high bitrate and up to 2 dB compared with the DISCOVER TDWZ video codec.

Magnetic Resonance Imaging (MRI) is a widely used, non-invasive imaging technology that plays a critical role in clinical diagnostics. Multi-modal MRI, which combines images from different modalities, enhances diagnostic accuracy by offering comprehensive tissue characterization. Meanwhile, multi-modal MRI enhances downstream tasks, like brain tumor segmentation and image reconstruction, by providing richer features. While recent advances in diffusion models (DMs) show potential for high-quality image translation, existing methods still struggle to preserve fine structural details and ensure accurate image synthesis in medical imaging. To address these challenges, we propose a Frequency-Aware Diffusion Model (FADM) for generating high-quality target modality MRI images from source modality images. The FADM incorporates a discrete wavelet transform within the diffusion model framework to extract both low- and high-frequency information from MRI images, enhancing the capture of tissue structural and textural features. Additionally, a wavelet downsampling layer and supervision module are incorporated to improve frequency awareness and optimize high-frequency detail extraction. Experimental results on the BraTS 2021 dataset and a 1.5T–3T MRI dataset demonstrate that the FADM outperforms existing generative models, particularly in preserving intricate brain structures and tumor regions while generating high-quality MRI images.