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DCC 2020

The Data Compression Conference (DCC) is an international forum for current work on data compression and related applications. Both theoretical and experimental work are of interest. Visit website.

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Compressing and Randomly Accessing Sequences
Adaptive Stream-based Entropy Coding
Entropy Coders Based on the Splitting of Lexicographic Intervals
Denoising Deep Boltzmann Machines: Compression for Deep Learning
Decompressing Lempel-Ziv compressed text
Compact Representation of Graphs with Small Bandwidth and Treedepth
Intra Prediction in the Emerging VVC Video Coding
Compressed Quadratization of Higher Order Binary Optimization Problems
Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication
Efficient Storage of Images onto DNA Using Vector Quantization
Residual Coding for Transform Skip Mode in Versatile Video Coding
Approximating Optimal Bidirectional Macro Schemes.
Machine-Learning-Based Method for Finding Optimal Video-Codec Configurations Using Physical Input-Video Features
EPIC: Context Adaptive Lossless Light Field Compression using Epipolar Plane Images
MODEL-INDEPENDENT RATE CONTROL FOR INTRA-CODING BASED ON PIECEWISE LINEAR APPROXIMATIONS
Convolutional Neural Network based Fast Intra Mode Prediction for H.266/FVC Video Coding
Noise-to-Compression Variational Autoencoder for Efficient End-to-End Optimized Image Coding
A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It
Super-Resolution in Compressive Coded Imaging Systems via l2 − l1 − l2 Minimization Under a Deep Learning Approach
Grammar compression with probabilistic context-free grammar

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