On-chip focal-plane compression for CMOS image sensors

Abstract

Miniature cameras have become an integral feature of todays' networked multimedia consumer products. The ever increasing demand for low cost ultra-compact 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 trade-offs 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 implement in CMOS 0.35μm technology. The processor was integrated with a 64 × 64 Time-to-First Spike (TFS) Digital Pixel Sensor (DPS) array. The processor 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.

Date of Award	2012
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology