Search Results for author: Tam Thuc Do

Found 5 papers, 0 papers with code

Interpretable Lightweight Transformer via Unrolling of Learned Graph Smoothness Priors

no code implementations6 Jun 2024 Tam Thuc Do, Parham Eftekhar, Seyed Alireza Hosseini, Gene Cheung, Philip Chou

We build interpretable and lightweight transformer-like neural networks by unrolling iterative optimization algorithms that minimize graph smoothness priors -- the quadratic graph Laplacian regularizer (GLR) and the $\ell_1$-norm graph total variation (GTV) -- subject to an interpolation constraint.

Learned Nonlinear Predictor for Critically Sampled 3D Point Cloud Attribute Compression

no code implementations22 Nov 2023 Tam Thuc Do, Philip A. Chou, Gene Cheung

We study 3D point cloud attribute compression via a volumetric approach: assuming point cloud geometry is known at both encoder and decoder, parameters $\theta$ of a continuous attribute function $f: \mathbb{R}^3 \mapsto \mathbb{R}$ are quantized to $\hat{\theta}$ and encoded, so that discrete samples $f_{\hat{\theta}}(\mathbf{x}_i)$ can be recovered at known 3D points $\mathbf{x}_i \in \mathbb{R}^3$ at the decoder.

Attribute Decoder

Volumetric 3D Point Cloud Attribute Compression: Learned polynomial bilateral filter for prediction

no code implementations22 Nov 2023 Tam Thuc Do, Philip A. Chou, Gene Cheung

We extend a previous study on 3D point cloud attribute compression scheme that uses a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \mapsto \mathbb{R}$, we quantize and encode parameters $\theta$ that characterize $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf(x))$ at known 3D points $\mathbf(x)$ at the decoder.

Attribute Decoder

Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attention

no code implementations1 Apr 2023 Tam Thuc Do, Philip A. Chou, Gene Cheung

We study 3D point cloud attribute compression using a volumetric approach: given a target volumetric attribute function $f : \mathbb{R}^3 \rightarrow \mathbb{R}$, we quantize and encode parameter vector $\theta$ that characterizes $f$ at the encoder, for reconstruction $f_{\hat{\theta}}(\mathbf{x})$ at known 3D points $\mathbf{x}$'s at the decoder.

Attribute Decoder

Hybrid Model-based / Data-driven Graph Transform for Image Coding

no code implementations2 Mar 2022 Saghar Bagheri, Tam Thuc Do, Gene Cheung, Antonio Ortega

Transform coding to sparsify signal representations remains crucial in an image compression pipeline.

Graph Learning Image Compression

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