Search Results for author:
Found 13 papers, 8 papers with code
PIM: Video Coding using Perceptual Importance Maps
The contributions of this work are threefold: (1) we introduce a web-tool which allows scalable collection of fine-grained perceptual importance, by having users interactively paint spatio-temporal maps over encoded videos; (2) we use this tool to collect a dataset with 178 videos with a total of 14443 frames of human annotated spatio-temporal importance maps over the videos; and (3) we use our curated dataset to train a lightweight machine learning model which can predict these spatio-temporal importance regions.
An Interactive Annotation Tool for Perceptual Video Compression
We use this tool to collect data in-the-wild (10 videos, 17 users) and utilize the obtained importance maps in the context of x264 coding to demonstrate that the tool can indeed be used to generate videos which, at the same bitrate, look perceptually better through a subjective study - and are 1. 9 times more likely to be preferred by viewers.
ELF-VC: Efficient Learned Flexible-Rate Video Coding
In this setting, for natural videos our approach compares favorably across the entire R-D curve under metrics PSNR, MS-SSIM and VMAF against all mainstream video standards (H. 264, H. 265, AV1) and all ML codecs.
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.
Neural Joint Source-Channel Coding
For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes.
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
NECST: Neural Joint Source-Channel Coding
For reliable transmission across a noisy communication channel, classical results from information theory show that it is asymptotically optimal to separate out the source and channel coding processes.
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory.
Robust Text-to-SQL Generation with Execution-Guided Decoding
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries.
