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Efficient Nonlinear Transforms for Lossy Image Compression

arXiv 1802.00847 ↗First posted 31 Jan 2018eess.IVcited by 20

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Abstract (arXiv)

We assess the performance of two techniques in the context of nonlinear transform coding with artificial neural networks, Sadam and GDN. Both techniques have been successfully used in state-of-the-art image compression methods, but their performance has not been individually assessed to this point. Together, the techniques stabilize the training procedure of nonlinear image transforms and increase their capacity to approximate the (unknown) rate-distortion optimal transform functions. Besides comparing their performance to established alternatives, we detail the implementation of both methods and provide open-source code along with the paper.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2018.

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Citing papers per year: 2021: 7; 2022: 5; 2023: 4; 2024: 1; 2025: 1; 2026: 2.

20 papers.

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  1. 2022ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Codingcited by 296
  2. 2021Checkerboard Context Model for Efficient Learned Image Compressioncited by 193
  3. 2022Wireless Deep Video Semantic Transmissioncited by 49
  4. 2021Exploring Autoencoder-based Error-bounded Compression for Scientific Datacited by 19
  5. 2021LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networkscited by 19
  6. 2022Post-Training Quantization for Cross-Platform Learned Image Compressioncited by 12
  7. 2021Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spacescited by 6
  8. 2023On the Importance of Denoising when Learning to Compress Imagescited by 5
  9. 2021A New Image Codec Paradigm for Human and Machine Usescited by 3
  10. 2023Volumetric Attribute Compression for 3D Point Clouds using Feedforward Network with Geometric Attentioncited by 3
  11. 2024COSMIC: Compress Satellite Images Efficiently via Diffusion Compensationcited by 3
  12. 2025Lightweight Embedded FPGA Deployment of Learned Image Compression with Knowledge Distillation and Hybrid Quantizationcited by 2
8 more
  1. 2021A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Spacecited by 1
  2. 2022Lossy Compression of Noisy Data for Private and Data-Efficient Learningcited by 1
  3. 2023Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Datacited by 1
  4. 2023Multi-spectral Entropy Constrained Neural Compression of Solar Imagerycited by 1
  5. 2021Quality and Complexity Assessment of Learning-Based Image Compression Solutionscited by 0
  6. 2022Transformations in Learned Image Compression from a Modulation Perspectivecited by 0
  7. 2026Deep learning-based compression of giga-resolution whole slide imagescited by 0
  8. 2026EVOLVE: Efficient Learned Volume Compression with Variable-Rate Encoding on a Cross-Domain Databasecited by 0