Vector quantization and signal compression
Vector quantization and signal compression
Elements of information theory
Elements of information theory
CMOS imagers: from phototransduction to image processi
CMOS imagers: from phototransduction to image processi
Viability of analog inner product operations in CMOS imagers
Proceedings of the 20th annual conference on Integrated circuits and systems design
Computation of the complexity of vector quantizers by affine modeling
Signal Processing
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We propose the use of a low-complexity vector quantizer for coding data vectors at the focal plane of CMOS image sensors, directly from analog pixels samples prior to A/D conversion. To be suitable for focal-plane image compression applications, the encoder must have a very low transistor count. Without considering the implementation errors in DPCM preprocessing of the image data, a test image is compressed by the proposed vector quantizer with peak signal-to-noise ratio around 26 dB at 0.73 bpp. The vector quantizer requires 145 transistors for each block of 4x4 pixels, and Monte Carlo simulation results in Cadence/Spectre led to the same image compression results that had been achieved in theoretical predictions.