28 citations · 49 across the 18 of their papers we have counts for
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eess.IV2023★ 1 cited
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-…
eess.IV2023
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…
cs.CV2023
ROI-based Deep Image Compression with Swin Transformers
Binglin Li, Jie Liang, Haisheng Fu +1
Encoding the Region Of Interest (ROI) with better quality than the background has many applications including video conferencing systems, video surveillance and object-oriented vis…