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ChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression
Haisheng Fu, Runyu Yang, Feng Ding +5
State-of-the-art learned image compression (LIC) schemes are increasingly based on hybrid CNN-transformer architectures. To further improve rate-distortion performance, we introduc…
ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality
Feng Ding, Haisheng Fu, Jie Liang +3
We study full-reference image quality assessment from a machine-centric perspective, where images are evaluated by how well they preserve information for downstream models. We form…
A Low-Complexity View Synthesis Distortion Estimation Method for 3D Video with Large Baseline Considerations
Chongyuan Bi, Jie Liang
Depth-image-based rendering is a key view synthesis algorithm in 3D video systems, which enables the synthesis of virtual views from texture images and depth maps. An efficient vie…
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation
Haisheng Fu, Feng Liang, Jie Liang +3
Deep learning-based image compression has made great progresses recently. However, many leading schemes use serial context-adaptive entropy model to improve the rate-distortion (R-…
Enhanced Residual SwinV2 Transformer for Learned Image Compression
Yongqiang Wang, Feng Liang, Haisheng Fu +3
Recently, the deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. However, a challenge of man…