Quantized Perceptual Compressed Sensing for Audio Signal Compression

Hossam Mohamed Kasem, Osumu Muta, Maha Elsabrouty, Hiroshi Frukawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Compressed Sensing (CS) has been widely used for multimedia processing to reduce the number of the measurements required to acquire signals that are spare or compressible sparse in some basis. CS provides good quality of the restored signal even when the signal is not completely sparse and even also at high compression ratio. However, classical CS assumes that the measurements are real-valued and have infinite-bit precision that requires impractical hardware implementation. Quantized CS provides a solution to this problem. Different quantized compressed sensing techniques are developed in literature and recovery is possible even if only 1-bit is used for the quantization. In this work, we propose using different quantization vlaues, including 1-bit compressed sensing for perceptual audio signal compression in perceptual systems in order to clarify the effect of the quantization process on the achievable quality of audio signal. Mean Opinion Score (MOS) is used as metric to compare the perceived quality of audio signal of 1-bit CS and classical CS. Simulations results show that reasonable performance is achieved for different quantization CS compared to quantized classical CS.

Original languageEnglish
Title of host publicationProceedings - DCC 2015
Subtitle of host publication2015 Data Compression Conference
EditorsJoan Serra-Sagrista, Michael W. Marcellin, Ali Bilgin, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781479984305
DOIs
Publication statusPublished - Jul 2 2015
Externally publishedYes
Event2015 Data Compression Conference, DCC 2015 - Snowbird, United States
Duration: Apr 7 2015Apr 9 2015

Publication series

NameData Compression Conference Proceedings
Volume2015-July
ISSN (Print)1068-0314

Other

Other2015 Data Compression Conference, DCC 2015
CountryUnited States
CitySnowbird
Period4/7/154/9/15

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Kasem, H. M., Muta, O., Elsabrouty, M., & Frukawa, H. (2015). Quantized Perceptual Compressed Sensing for Audio Signal Compression. In J. Serra-Sagrista, M. W. Marcellin, A. Bilgin, & J. A. Storer (Eds.), Proceedings - DCC 2015: 2015 Data Compression Conference [7149316] (Data Compression Conference Proceedings; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCC.2015.52