Name
Title | ||
|---|---|---|
Near-Lossless Compression for Sparse Source Using Convolutional Low Density Generator Matrix Codes |
Abstract | ||
|---|---|---|
In this paper, we present a new coding approach to near-lossless compression for binary sparse sources by using a special class of low density generator matrix (LDGM) codes. On the theoretical side, we proved that such a class of block LDGM codes are universal in the sense that any source with an entropy less than the coding rate can be compressed and reconstructed with an arbitrarily low bit-erro... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/DCC50243.2021.00040 | 2021 Data Compression Conference (DCC) |
Keywords | DocType | ISSN |
Convolutional codes,Data compression,Generators,Encoding,Iterative algorithms,Entropy,Complexity theory | Conference | 1068-0314 |
ISBN | Citations | PageRank |
978-1-6654-0333-7 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tingting Zhu | 1 | 0 | 0.34 |
| Xiao Ma | 2 | 487 | 64.77 |