7 citations · 8 across the 10 of their papers we have counts for
11 papers
CSGaussian: Progressive Rate-Distortion Compression and Segmentation for 3D Gaussian Splatting
Yu-Jen Tseng, Chia-Hao Kao, Jing-Zhong Chen +4
We present the first unified framework for rate-distortion-optimized compression and segmentation of 3D Gaussian Splatting (3DGS). While 3DGS has proven effective for both real-tim…
MH-LVC: Multi-Hypothesis Temporal Prediction for Learned Conditional Residual Video Coding
Huu-Tai Phung, Zong-Lin Gao, Yi-Chen Yao +5
This work, termed MH-LVC, presents a multi-hypothesis temporal prediction scheme that employs long- and short-term reference frames in a conditional residual video coding framework…
End-to-End Semantic Preservation in Text-Aware Image Compression Systems
Stefano Della Fiore, Alessandro Gnutti, Marco Dalai +2
Traditional image compression methods aim to reconstruct images for human perception, prioritizing visual fidelity over task relevance. In contrast, Coding for Machines focuses on…
Is JPEG AI going to change image forensics?
Edoardo Daniele Cannas, Sara Mandelli, Nataša Popović +4
In this paper, we investigate the counter-forensic effects of the new JPEG AI standard based on neural image compression, focusing on two critical areas: deepfake image detection a…
Learning Optimal Linear Block Transform by Rate Distortion Minimization
Alessandro Gnutti, Chia-Hao Kao, Wen-Hsiao Peng +1
Linear block transform coding remains a fundamental component of image and video compression. Although the Discrete Cosine Transform (DCT) is widely employed in all current compres…
Variable-size Symmetry-based Graph Fourier Transforms for image compression
Alessandro Gnutti, Fabrizio Guerrini, Riccardo Leonardi +1
Modern compression systems use linear transformations in their encoding and decoding processes, with transforms providing compact signal representations. While multiple data-depend…
Bridging Compressed Image Latents and Multimodal Large Language Models
Chia-Hao Kao, Cheng Chien, Yu-Jen Tseng +5
This paper presents the first-ever study of adapting compressed image latents to suit the needs of downstream vision tasks that adopt Multimodal Large Language Models (MLLMs). MLLM…
Transformer-based Learned Image Compression for Joint Decoding and Denoising
Yi-Hsin Chen, Kuan-Wei Ho, Shiau-Rung Tsai +4
This work introduces a Transformer-based image compression system. It has the flexibility to switch between the standard image reconstruction and the denoising reconstruction from…
LiDAR Depth Map Guided Image Compression Model
Alessandro Gnutti, Stefano Della Fiore, Mattia Savardi +3
The incorporation of LiDAR technology into some high-end smartphones has unlocked numerous possibilities across various applications, including photography, image restoration, augm…
CANF-VC: Conditional Augmented Normalizing Flows for Video Compression
Yung-Han Ho, Chih-Peng Chang, Peng-Yu Chen +2
This paper presents an end-to-end learning-based video compression system, termed CANF-VC, based on conditional augmented normalizing flows (CANF). Most learned video compression s…
The maximum cardinality of trifferent codes with lengths 5 and 6
Stefano Della Fiore, Alessandro Gnutti, Sven Polak
A code is said to be trifferent with length when for any three distinct elements of there exists a coordinate in which they…