C3: High-performance and low-complexity neural compression from a single image or video
Most neural compression models are trained on large datasets of images or videos in order
to generalize to unseen data. Such generalization typically requires large and expressive …
to generalize to unseen data. Such generalization typically requires large and expressive …
D'OH: Decoder-Only Random Hypernetworks for Implicit Neural Representations
Deep implicit functions have been found to be an effective tool for efficiently encoding all
manner of natural signals. Their attractiveness stems from their ability to compactly represent …
manner of natural signals. Their attractiveness stems from their ability to compactly represent …
Breaking the Barriers of One-to-One Usage of Implicit Neural Representation in Image Compression: A Linear Combination Approach with Performance Guarantees
In an era where the exponential growth of image data driven by the Internet of Things (IoT) is
outpacing traditional storage solutions, this work explores and advances the potential of …
outpacing traditional storage solutions, this work explores and advances the potential of …
[PDF][PDF] Levels-of-Experts based on Hard Segmentations for Improving Implicit Neural Networks
MJ Oh, SH Bae - 방송공학회논문지, 2024 - ksbe-jbe.org
Implicit neural representation is used in various applications such as super-resolution, 3D
reconstruction, and image compression. Because continuous signals can be generated at …
reconstruction, and image compression. Because continuous signals can be generated at …
Lossy Compression with Machine Learning: Techniques and Fundamental Limits
Y Yang - 2024 - search.proquest.com
The exponential growth in global data creation and transmission necessitates increasingly
powerful data compression algorithms. Neural compression, which leverages neural …
powerful data compression algorithms. Neural compression, which leverages neural …