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Found 9 papers, 2 papers with code
S2LIC: Learned Image Compression with the SwinV2 Block, Adaptive Channel-wise and Global-inter Attention Context
In this paper, we propose an Adaptive Channel-wise and Global-inter attention Context (ACGC) entropy model, which can efficiently achieve dual feature aggregation in both inter-slice and intraslice contexts.
Fast and High-Performance Learned Image Compression With Improved Checkerboard Context Model, Deformable Residual Module, and Knowledge Distillation
Then we only encode non-zero channels in the encoding and decoding process, which can greatly reduce the encoding and decoding time.
Enhanced Residual SwinV2 Transformer for Learned Image Compression
In particular, our method achieves comparable results while reducing model complexity by 56% compared to these recent methods.
ROI-based Deep Image Compression with Swin Transformers
Encoding the Region Of Interest (ROI) with better quality than the background has many applications including video conferencing systems, video surveillance and object-oriented vision tasks.
Learned Image Compression with Generalized Octave Convolution and Cross-Resolution Parameter Estimation
In addition, these methods based on the context-adaptive entropy model cannot be accelerated in the decoding process by parallel computing devices, e. g. FPGA or GPU.
Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering
Based on this observation, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only needs one stage of MSRB to yield satisfactory reconstruction, thereby reducing the decoding complexity without sacrifcing performance.
Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image.
Improved Hybrid Layered Image Compression using Deep Learning and Traditional Codecs
Recently deep learning-based methods have been applied in image compression and achieved many promising results.
A Deep Image Compression Framework for Face Recognition
In this paper, we propose a deep convolutional autoencoder compression network for face recognition tasks.
