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Jun 23, 2022 · We propose shift-addition parallel modules (SAPMs), including SAPM-E for the encoder and SAPM-D for the decoder, to largely reduce the energy consumption.
Jun 23, 2022 · We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, ...
Jun 23, 2022 · UNIVERSAL LEARNED IMAGE COMPRESSION WITH LOW COMPUTATIONAL COST. Bowen Li1, Yao Xin2, Youneng Bao1, Fanyang Meng2, Yongsheng Liang1∗, Wen ...
The Mandt lab introduces a novel approach to neural image compression, significantly reducing decoding complexity while maintaining competitive rate-distortion ...
Missing: Universal | Show results with:Universal
Apr 6, 2022 · There are known methods for dictionary learning. One of the famous is the K-SVD method. When one takes large set of native images and iterates ...
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UNIVERSAL LEARNED IMAGE COMPRESSION WITH LOW COMPUTATIONAL COST. YX Bowen Li, Y Bao, F Meng, Y Liang, W Tan. 2022. Transformations in Learned Image Compression ...
4 days ago · Recent methods utilize convolutional neural networks (CNN), variational autoencoders (VAE), invertible neural networks (INN), and transformers.
Oct 27, 2023 · Our proposed method exhibits universality across diverse image datasets. Extensive results demonstrate that this paradigm significantly ...
Missing: Cost. | Show results with:Cost.
This paper introduces novel image compression models, MGIC and RMGIC, which consist of the Residual Neighborhood-based Attention Module (RNAM), Multi-Scale ...
(ICIP 2023) Learned Image Compression with Large Capacity and Low ... (ICASSP 2022) AdderIC: Towards Low Computation Cost Image Compression Li ...