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Found 7 papers, 6 papers with code
Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text
Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure.
Reducing latency and bandwidth for video streaming using keypoint extraction and digital puppetry
COVID-19 has made video communication one of the most important modes of information exchange.
DZip: improved general-purpose lossless compression based on novel neural network modeling
We propose DZip, a general-purpose compressor for sequential data that exploits the well-known modeling capabilities of neural networks (NNs) for prediction, followed by arithmetic coding.
LFZip: Lossy compression of multivariate floating-point time series data via improved prediction
Time series data compression is emerging as an important problem with the growth in IoT devices and sensors.
Tutorial on algebraic deletion correction codes
It is not intended to be an exhaustive survey of works on deletion channel, but more as a tutorial to some of the important and cute ideas in this area.
Data Structures and Algorithms Information Theory Signal Processing Information Theory
DeepZip: Lossless Data Compression using Recurrent Neural Networks
We combine recurrent neural network predictors with an arithmetic coder and losslessly compress a variety of synthetic, text and genomic datasets.
Towards improved lossy image compression: Human image reconstruction with public-domain images
The images, results and additional data are available at https://compression. stanford. edu/human-compression
