5 papers
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…
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…
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…
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…