A theory of incremental compression

Partition Reduction for Lossy Data Compression Problem

We consider the computational aspects of lossy data compression problem, where the compression error is determined by a cover of the data space. We propose an algorithm which reduces the number of partitions needed to find the entropy with respect to the compression error. In particular, we show that, in the case of finite cover, the entropy is attained on some partition. We give an algorithmic construction of such partition.

Effects of Sequence Partitioning on Compression Rate

In the paper, a theoretical work is done for investigating effects of splitting data sequence into packs of data set. We proved that a partitioning of data sequence is possible to find such that the entropy rate at each subsequence is lower than entropy rate of the source. Effects of sequence partitioning on overall compression rate are argued on the bases of partitioning statistics, and then, an optimization problem for an optimal partition is defined to improve overall compression rate of a sequence.

Green DetNet: Computation and Memory efficient DetNet using Smart Compression and Training

This paper introduces an incremental training framework for compressing popular Deep Neural Network (DNN) based unfolded multiple-input-multiple-output (MIMO) detection algorithms like DetNet. The idea of incremental training is explored to select the optimal depth while training. To reduce the computation requirements or the number of FLoating point OPerations (FLOPs) and enforce sparsity in weights, the concept of structured regularization is explored using group LASSO and sparse group LASSO. Our methods lead to an astounding $98.9\%$ reduction in memory requirement and $81.63\%$ reduction i…

ECG Signal Compression and Optimization in Remote Monitoring Networks

We proposed a practical ECG compression system which is beneficial for tele-monitoring cardiovascular diseases. There are two steps in the compression framework. First, we partition ECG signal into segments according to R- to R-wave periods. The partition aims at achieving more stable statistical features between segments of ECG signal which is beneficial for saving bit rates. After the partition, we optimize the bit rate in the sense of minimizing ECG reconstruction error under a constraint of consumed bits. From the experiment results, the proposed compression scheme is able to reduce the co…

On optimally partitioning a text to improve its compression

In this paper we investigate the problem of partitioning an input string T in such a way that compressing individually its parts via a base-compressor C gets a compressed output that is shorter than applying C over the entire T at once. This problem was introduced in the context of table compression, and then further elaborated and extended to strings and trees. Unfortunately, the literature offers poor solutions: namely, we know either a cubic-time algorithm for computing the optimal partition based on dynamic programming, or few heuristics that do not guarantee any bounds on the efficacy of …

二元循环梯度编码：精确恢复与压缩鲁棒性

Binary Cyclic Gradient Coding: Exact Recovery and Robustness to Compression

提出二元循环梯度编码（BCGC），其编码矩阵为二元循环矩阵，在FRC、GFRC无法恢复精确梯度时可实现精确恢复，且在压缩下更稳定，异构数据上精度更高。