Adaptive sampling for compressed sensing based image compression
Tutkimustuotos › › vertaisarvioitu
Yksityiskohdat
| Alkuperäiskieli | Englanti |
|---|---|
| Otsikko | 2014 IEEE International Conference on Multimedia and Expo (ICME), 14-18 July 2014, Chengdu |
| DOI - pysyväislinkit | |
| Tila | Julkaistu - 3 syyskuuta 2014 |
| OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
| Tapahtuma | IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO - Kesto: 1 tammikuuta 1900 → … |
Conference
| Conference | IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO |
|---|---|
| Ajanjakso | 1/01/00 → … |
Tiivistelmä
The compressed sensing (CS) theory shows that a sparse signal can be recovered at a sampling rate that is (much) lower than the required Nyquist rate. In practice, many image signals are sparse in a certain domain, and because of this, the CS theory has been successfully applied to the image compression in the past few years. The most popular CS-based image compression scheme is the block-based CS (BCS). In this paper, we focus on the design of an adaptive sampling mechanism for the BCS through a deep analysis of the statistical information of each image block. Specifically, this analysis will be carried out at the encoder side (which needs a few overhead bits) and the decoder side (which requires a feedback to the encoder side), respectively. Two corresponding solutions will be compared carefully in our work. We also present experimental results to show that our proposed adaptive method offers a remarkable quality improvement compared with the traditional BCS schemes.