Title :
Compressive Acquisition CMOS Image Sensor: From the Algorithm to Hardware Implementation
Author :
Zhang, Milin ; Bermak, Amine
Author_Institution :
Electron. & Comput. Eng. Dept., Hong Kong Univ. of Sci. & Technol., Hong Kong, China
fDate :
3/1/2010 12:00:00 AM
Abstract :
In this paper, a new design paradigm referred to as compressive acquisition CMOS image sensors is introduced. The idea consists of compressing the data within each pixel prior to storage, and hence, reducing the size of the memory required for digital pixel sensor. The proposed compression algorithm uses a block-based differential coding scheme in which differential values are captured and quantized online. A time-domain encoding scheme is used in our CMOS image sensor in which the brightest pixel within each block fires first and is selected as the reference pixel. The differential values between subsequent pixels and the reference within each block are calculated and quantized, using a reduced number of bits as their dynamic range is compressed. The proposed scheme enables reduced error accumulation as full precision is used at the start of each block, while also enabling reduced memory requirement, and hence, enabling significant silicon area saving. A mathematical model is derived to analyze the performance of the algorithm. Experimental results on a field-programmable gate-array (FPGA) platform illustrate that the proposed algorithm enables more than 50% memory saving at a peak signal-to-noise ratio level of 30 dB with 1.5 bit per pixel.
Keywords :
CMOS image sensors; data compression; field programmable gate arrays; block-based differential coding scheme; compression algorithm; compressive acquisition CMOS image sensor; data compression; digital pixel sensor; error accumulation; field-programmable gate-array platform; peak signal-to-noise ratio level; reduced memory requirement; time-domain encoding scheme; Compressive acquisition; differential pulse code modulation (DPCM); digital pixel sensors (DPSs); online block-based compression algorithm;
Journal_Title :
Very Large Scale Integration (VLSI) Systems, IEEE Transactions on
DOI :
10.1109/TVLSI.2008.2011489