By Professor David Scott Taubman

Preprints

Li X; Naman A; Taubman D, 2026, Neural Network Assisted Lifting Steps For Improved Fully Scalable Lossy Image Compression in JPEG 2000, http://dx.doi.org/10.48550/arxiv.2403.01647

Gray JL; Naman AT; Taubman DS, 2024, Multi-view Disparity Estimation Using a Novel Gradient Consistency Model, http://dx.doi.org/10.48550/arxiv.2405.17029

Li X; Naman A; Taubman D, 2024, Exploration of Learned Lifting-Based Transform Structures for Fully Scalable and Accessible Wavelet-Like Image Compression, http://dx.doi.org/10.48550/arxiv.2402.18761

Karmakar P; Murshed M; Paul M; Taubman D, 2022, Efficient Motion Modelling with Variable-sized blocks from Hierarchical Cuboidal Partitioning, http://dx.doi.org/10.48550/arxiv.2208.13137

Young SI; Zhe W; Taubman D; Girod B, 2021, Transform Quantization for CNN (Convolutional Neural Network) Compression, http://dx.doi.org/10.48550/arxiv.2009.01174

Gray JL; Naman AT; Taubman DS, 2021, Welsch Based Multiview Disparity Estimation, http://dx.doi.org/10.48550/arxiv.2110.00803

Ahmmed A; Paul M; Murshed M; Taubman D, 2021, Human-Machine Collaborative Video Coding Through Cuboidal Partitioning, http://dx.doi.org/10.48550/arxiv.2102.01307

Kitaeff VV; Cannon A; Wicenec A; Taubman D, 2014, Astronomical Imagery: Considerations For a Contemporary Approach with JPEG2000, http://dx.doi.org/10.48550/arxiv.1403.2801

Kitaeff S; Wicenec A; Wu C; Taubman D, 2013, Extremely Large Images: Considerations for Contemporary Approach, http://dx.doi.org/10.48550/arxiv.1307.5123

Jayasooriya K; Jenner SP; Marasinghe P; Senanayake U; Saadat H; Taubman D; Ragel R; Gamaarachchi H; Deveson IW, A new compression strategy to reduce the size of nanopore sequencing data, http://dx.doi.org/10.1101/2024.10.02.616377

Karmakar P; Murshed M; Paul M; Taubman D, An efficient video coding with object-bounded motion estimation using cuboid-based variable-sized block partitioning, http://dx.doi.org/10.21203/rs.3.rs-3302367/v1

Ahmmed A; Paul M; Murshed M; Taubman D, Human-Machine Collaborative Video Coding Through Cuboidal Partitioning, http://dx.doi.org/10.31224/osf.io/v7y2r

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