Manu Mathew Thomas

Akshay Jindal, Nabil Sadaka, Manu Mathew Thomas et al.

While existing video and image quality datasets have extensively studied natural videos and traditional distortions, the perception of synthetic content and modern rendering artifacts remains underexplored. We present a novel video quality dataset focused on distortions introduced by advanced rendering techniques, including neural supersampling, novel-view synthesis, path tracing, neural denoising, frame interpolation, and variable rate shading. Our evaluations show that existing full-reference quality metrics perform sub-optimally on these distortions, with a maximum Pearson correlation of 0.78. Additionally, we find that the feature space of pre-trained 3D CNNs aligns strongly with human perception of visual quality. We propose CGVQM, a full-reference video quality metric that significantly outperforms existing metrics while generating both per-pixel error maps and global quality scores. Our dataset and metric implementation is available at https://github.com/IntelLabs/CGVQM.

Ran Xu, Manu Mathew Thomas, Alex Leow et al.

We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions in the brain. These patterns are calculated by processing raw data retrieved functional magnetic resonance imaging (fMRI) scans, which creates a network of weighted edges between each brain region, where the weight indicates how likely these regions are to activate synchronously. In particular, we support the analysis needs of clinical psychologists, who examine these modular affiliations and weighted edges and their temporal dynamics, utilizing them to understand relationships between neurological disorders and brain activity, which could have a significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of TempoCave, which supports a range of comparative tasks, and runs both in a desktop mode and in an immersive mode. Furthermore, we present a real-world use case that analyzes pre- and post-treatment connectome datasets from 27 subjects in a clinical study investigating the use of cognitive behavior therapy to treat major depression disorder, indicating that TempoCave can provide new insight into the dynamic behavior of the human brain.