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Found 12 papers, 2 papers with code
Editorial: Introduction to the Issue on Deep Learning for Image/Video Restoration and Compression
Recent works have shown that learned models can achieve significant performance gains, especially in terms of perceptual quality measures, over traditional methods.
Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference
We show results that are within 1. 6% of the reported, non-quantized performance on MobileNet using only 40 entries in our table.
Neural Image Decompression: Learning to Render Better Image Previews
A rapidly increasing portion of Internet traffic is dominated by requests from mobile devices with limited- and metered-bandwidth constraints.
No Multiplication? No Floating Point? No Problem! Training Networks for Efficient Inference
For successful deployment of deep neural networks on highly--resource-constrained devices (hearing aids, earbuds, wearables), we must simplify the types of operations and the memory/power resources used during inference.
Representing Images in 200 Bytes: Compression via Triangulation
A rapidly increasing portion of internet traffic is dominated by requests from mobile devices with limited and metered bandwidth constraints.
Image-Dependent Local Entropy Models for Learned Image Compression
The leading approach for image compression with artificial neural networks (ANNs) is to learn a nonlinear transform and a fixed entropy model that are optimized for rate-distortion performance.
Spatially adaptive image compression using a tiled deep network
Deep neural networks represent a powerful class of function approximators that can learn to compress and reconstruct images.
Target-Quality Image Compression with Recurrent, Convolutional Neural Networks
Our methods introduce a multi-pass training method to combine the training goals of high-quality reconstructions in areas around stop-code masking as well as in highly-detailed areas.
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0 ), WebP, JPEG2000, and JPEG as measured by MS-SSIM.
Traffic Lights with Auction-Based Controllers: Algorithms and Real-World Data
Real-time optimization of traffic flow addresses important practical problems: reducing a driver's wasted time, improving city-wide efficiency, reducing gas emissions and improving air quality.
Full Resolution Image Compression with Recurrent Neural Networks
As far as we know, this is the first neural network architecture that is able to outperform JPEG at image compression across most bitrates on the rate-distortion curve on the Kodak dataset images, with and without the aid of entropy coding.
Variable Rate Image Compression with Recurrent Neural Networks
A large fraction of Internet traffic is now driven by requests from mobile devices with relatively small screens and often stringent bandwidth requirements.
