predictive quantization
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Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1354
Author(s):  
Qunlin Chen ◽  
Derong Chen ◽  
Jiulu Gong

Block compressed sensing (BCS) is a promising technology for image sampling and compression for resource-constrained applications, but it needs to balance the sampling rate and quantization bit-depth for a bit-rate constraint. In this paper, we summarize the commonly used CS quantization frameworks into a unified framework, and a new bit-rate model and a model of the optimal bit-depth are proposed for the unified CS framework. The proposed bit-rate model reveals the relationship between the bit-rate, sampling rate, and bit-depth based on the information entropy of generalized Gaussian distribution. The optimal bit-depth model can predict the optimal bit-depth of CS measurements at a given bit-rate. Then, we propose a general algorithm for choosing sampling rate and bit-depth based on the proposed models. Experimental results show that the proposed algorithm achieves near-optimal rate-distortion performance for the uniform quantization framework and predictive quantization framework in BCS.



2020 ◽  
Vol 58 (8) ◽  
pp. 5575-5587 ◽  
Author(s):  
Michele Martone ◽  
Nicola Gollin ◽  
Michelangelo Villano ◽  
Paola Rizzoli ◽  
Gerhard Krieger


Author(s):  
Nicola Gollin ◽  
Michele Martone ◽  
Michelangelo Villano ◽  
Paola Rizzoli ◽  
Gerhard Krieger


Author(s):  
Kumar Desappan ◽  
Mihir Mody ◽  
Manu Mathew ◽  
Pramod Swami ◽  
Praveen Eppa


2015 ◽  
Vol 22 (2) ◽  
pp. 234-238 ◽  
Author(s):  
Stefan Schwarz ◽  
Markus Rupp


2012 ◽  
Vol 50 (4) ◽  
pp. 1340-1348 ◽  
Author(s):  
Takeshi Ikuma ◽  
Mort Naraghi-Pour ◽  
Thomas Lewis


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