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Found 8 papers, 2 papers with code
TFIC: End-to-End Text-Focused Image Compression for Coding for Machines
Traditional image compression methods aim to faithfully reconstruct images for human perception.
Is JPEG AI going to change image forensics?
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 and image splicing localization.
Learning Optimal Linear Block Transform by Rate Distortion Minimization
Linear block transform coding remains a fundamental component of image and video compression.
Variable-size Symmetry-based Graph Fourier Transforms for image compression
Modern compression systems use linear transformations in their encoding and decoding processes, with transforms providing compact signal representations.
Bridging Compressed Image Latents and Multimodal Large Language Models
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).
Transformer-based Learned Image Compression for Joint Decoding and Denoising
Instead of training separate decoders for these tasks, we incorporate two add-on modules to adapt a pre-trained image decoder from performing the standard image reconstruction to joint decoding and denoising.
LiDAR Depth Map Guided Image Compression Model
In this paper, we introduce a novel direction that harnesses LiDAR depth maps to enhance the compression of the corresponding RGB camera images.
CANF-VC: Conditional Augmented Normalizing Flows for Video Compression
CANF-VC represents a new attempt that leverages the conditional ANF to learn a video generative model for conditional inter-frame coding.
