Search Results for author:
Found 21 papers, 8 papers with code
KBNet: Kernel Basis Network for Image Restoration
In this paper, we propose a kernel basis attention (KBA) module, which introduces learnable kernel bases to model representative image patterns for spatial information aggregation.
Ranked #1 on
Color Image Denoising
on McMaster sigma50
ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding
Recently, learned image compression techniques have achieved remarkable performance, even surpassing the best manually designed lossy image coders.
Ranked #1 on
Image Compression
on kodak
Checkerboard Context Model for Efficient Learned Image Compression
To the best of our knowledge, this is the first exploration on parallelization-friendly spatial context model for learned image compression.
PO-ELIC: Perception-Oriented Efficient Learned Image Coding
In the past years, learned image compression (LIC) has achieved remarkable performance.
Idempotence and Perceptual Image Compression
However, we find that theoretically: 1) Conditional generative model-based perceptual codec satisfies idempotence; 2) Unconditional generative model with idempotence constraint is equivalent to conditional generative codec.
Bit Allocation using Optimization
In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC).
GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting
In response, we propose a groundbreaking paradigm of image representation and compression by 2D Gaussian Splatting, named GaussianImage.
Boosting Neural Representations for Videos with a Conditional Decoder
Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing, showing remarkable versatility across various video tasks.
TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding
Existing works usually adopt dynamic graph networks to indirectly model the intra/inter-modal interactions, making the model difficult to distinguish the referred object from distractors due to the monolithic representations of visual and linguistic contents.
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning
Learned image compression is making good progress in recent years.
Post-Training Quantization Is All You Need to Perform Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
Post-Training Quantization for Cross-Platform Learned Image Compression
It has been witnessed that learned image compression has outperformed conventional image coding techniques and tends to be practical in industrial applications.
Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain
JPEG is a popular image compression method widely used by individuals, data center, cloud storage and network filesystems.
Evaluating the Practicality of Learned Image Compression
On CPU, the latency of our implementation is comparable with JPEG XL.
Flexible Neural Image Compression via Code Editing
Neural image compression (NIC) has outperformed traditional image codecs in rate-distortion (R-D) performance.
Multi-Sample Training for Neural Image Compression
This paper considers the problem of lossy neural image compression (NIC).
Conditional Perceptual Quality Preserving Image Compression
We propose conditional perceptual quality, an extension of the perceptual quality defined in \citet{blau2018perception}, by conditioning it on user defined information.
Efficient Learned Lossless JPEG Recompression
Second, we propose pipeline parallel context model (PPCM) and compressed checkerboard context model (CCCM) for the effective conditional modeling and efficient decoding within luma and chroma components.
Content-aware Masked Image Modeling Transformer for Stereo Image Compression
Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations.
Consistency Models Improve Diffusion Inverse Solvers
In addition, we propose a new family of DIS using pure CM.
Task-Aware Encoder Control for Deep Video Compression
Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task.
