On-chip focal-plane compression for CMOS image sensors

Yan Wang

    Research output: ThesisDoctoral Thesis

    336 Downloads (Pure)

    Abstract

    Miniature cameras have become an integral feature of todays’ networked multimedia

    consumer products. The ever increasing demand for low cost ultracompact

    multimedia products is behind a growing effort towards integrating the

    different circuit components of a camera system onto a single-chip. Such an integration

    has become possible using microelectronics industry standard CMOS

    fabrication process, which enables the fabrication of a CMOS pixel array together

    with image processing circuitry. This thesis investigates the challenges of

    integrating the image compression block into a CMOS camera. The direct implementation

    of standard image compression algorithms like JPEG would result

    in prohibitively large power and silicon area budgets because image compression

    standards like JPEG are computationally and resource intensive.

    To address this issue, this thesis introduces a number of hardware friendly image

    compression schemes suitable for integration with CMOS imagers. Depending

    on the target application, the different proposed schemes can offer different tradeoffs

    between image quality, memory requirements, silicon and power budget.

    A novel image compression processor based on predictive coding, adaptive

    quantization and Quadrant Tree Decomposition (QTD) algorithms featuring low

    complexity, low power, and high compactness was proposed and successfully implemented

    in CMOS 0.35μm technology. The processor was integrated with a

    64 × 64 Time-to-First Spike (TFS) Digital Pixel Sensor (DPS) array. The pro

    cessor occupies 0.55mm2 silicon area and consumes 2 mW at 30 frames/s.

    A second image compression scheme based on visual pattern image coding

    (VPIC) and optimized for TFS DPS was subsequently proposed to further improve

    image quality. Intensive multiplication and square root computations are

    replaced with addition and shift operations. Image quality with Lena image reported

    was 29 dB at 0.875 Bit-Per-Pixel (BPP).

    The second part of the thesis explores potential applications of the newly introduced

    compressive sampling paradigm. The latter addresses the inefficiency

    of traditional signal processing pipeline which involves sampling followed by compression.

    Exploiting compressive sampling theory, we propose novel spatial and

    bit domain schemes that simultaneously sample and compress images with no

    computation. Compressed images were reconstructed using l1-norm minimization

    linear programming algorithms. Reported experimental results from the

    implemented FPGA platform show reconstruction quality of 29 dB at 2 BPP for

    256 × 256 image.

    Finally, a novel image compression method based on vector quantization (VQ)

    with shrunk codeword and reduced number of searches was proposed and implemented

    in FPGA. The quality of Lena image reported was 29.26 dB at 0.5625

    BPP, with 0.57 dB sacrifice but 96.54%, 96.72%, 96.8%, and 99.47% reduction

    in the number of additions, subtractions, multiplications, and square roots operations,

    respectively, required by conventional full search VQ.


    Original languageEnglish
    QualificationDoctor of Philosophy
    Publication statusUnpublished - 2012

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