Efficient Feature Compression for Edge-Cloud Systems

Author:

Duan Zhihao1,Zhu Fengqing1

Affiliation:

1. Purdue University,Elmore Family School of Electrical and Computer Engineering,West Lafayette,Indiana,U.S.A

Publisher

IEEE

Reference23 articles.

1. Back-and-forth prediction for deep tensor compression;choi;IEEE International Conference on Acoustics Speech and Signal Processing,2020

2. Lightweight Compression Of Neural Network Feature Tensors For Collaborative Intelligence

3. Lossy compression for lossless prediction;dubois;Advances in neural information processing systems,2021

4. Density modeling of images using a generalized normalization transformation;ballé;International Conference on Learning Representations,2016

5. Deep Residual Learning for Image Recognition

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1. Reconstruction-free Image Compression for Machine Vision via Knowledge Transfer;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-07-17

2. An Effective Entropy Model for Semantic Feature Compression;2024 Picture Coding Symposium (PCS);2024-06-12

3. Balancing the Encoder and Decoder Complexity in Image Compression for Classification;2024-04-22

4. Learned Point Cloud Compression for Classification;2023 IEEE 25th International Workshop on Multimedia Signal Processing (MMSP);2023-09-27

5. Flexible Variable-Rate Image Feature Compression for Edge-Cloud Systems;2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW);2023-07

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