58 citations · 94 across the 12 of their papers we have counts for
16 papers
What Matters in Practical Learned Image Compression
Kedar Tatwawadi, Parisa Rahimzadeh, Zhanghao Sun +5
One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human v…
Drop-In Perceptual Optimization for 3D Gaussian Splatting
Ezgi Ozyilkan, Zhiqi Chen, Oren Rippel +2
Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry r…
PIM: Video Coding using Perceptual Importance Maps
Evgenya Pergament, Pulkit Tandon, Oren Rippel +6
Human perception is at the core of lossy video compression, with numerous approaches developed for perceptual quality assessment and improvement over the past two decades. In the d…
An Interactive Annotation Tool for Perceptual Video Compression
Evgenya Pergament, Pulkit Tandon, Kedar Tatwawadi +6
Human perception is at the core of lossy video compression and yet, it is challenging to collect data that is sufficiently dense to drive compression. In perceptual quality assessm…
ELF-VC: Efficient Learned Flexible-Rate Video Coding
Oren Rippel, Alexander G. Anderson, Kedar Tatwawadi +3
While learned video codecs have demonstrated great promise, they have yet to achieve sufficient efficiency for practical deployment. In this work, we propose several novel ideas fo…
Reducing latency and bandwidth for video streaming using keypoint extraction and digital puppetry
Roshan Prabhakar, Shubham Chandak, Carina Chiu +4
COVID-19 has made video communication one of the most important modes of information exchange. While extensive research has been conducted on the optimization of the video streamin…
DZip: improved general-purpose lossless compression based on novel neural network modeling
Mohit Goyal, Kedar Tatwawadi, Shubham Chandak +1
We consider lossless compression based on statistical data modeling followed by prediction-based encoding, where an accurate statistical model for the input data leads to substanti…
LFZip: Lossy compression of multivariate floating-point time series data via improved prediction
Shubham Chandak, Kedar Tatwawadi, Chengtao Wen +3
Time series data compression is emerging as an important problem with the growth in IoT devices and sensors. Due to the presence of noise in these datasets, lossy compression can o…
Tutorial on algebraic deletion correction codes
Kedar Tatwawadi, Shubham Chandak
The deletion channel is known to be a notoriously diffcult channel to design error-correction codes for. In spite of this difficulty, there are some beautiful code constructions wh…
Optimal Communication Rates and Combinatorial Properties for Common Randomness Generation
Yanjun Han, Kedar Tatwawadi, Gowtham R. Kurri +3
We study common randomness generation problems where players aim to generate same sequences of random coin flips where some subsets of the players share an independent common c…
DeepZip: Lossless Data Compression using Recurrent Neural Networks
Mohit Goyal, Kedar Tatwawadi, Shubham Chandak +1
Sequential data is being generated at an unprecedented pace in various forms, including text and genomic data. This creates the need for efficient compression mechanisms to enable…
Neural Joint Source-Channel Coding
Kristy Choi, Kedar Tatwawadi, Aditya Grover +2
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 chan…
Towards improved lossy image compression: Human image reconstruction with public-domain images
Ashutosh Bhown, Soham Mukherjee, Sean Yang +5
Lossy image compression has been studied extensively in the context of typical loss functions such as RMSE, MS-SSIM, etc. However, compression at low bitrates generally produces un…
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
Tianze Shi, Kedar Tatwawadi, Kaushik Chakrabarti +3
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 inv…
Robust Text-to-SQL Generation with Execution-Guided Decoding
Chenglong Wang, Kedar Tatwawadi, Marc Brockschmidt +4
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries. We introduce a new mechanism, execution guidance, to le…
Minimax redundancy for Markov chains with large state space
Kedar Shriram Tatwawadi, Jiantao Jiao, Tsachy Weissman
For any Markov source, there exist universal codes whose normalized codelength approaches the Shannon limit asymptotically as the number of samples goes to infinity. This paper inv…