Architecture of a polymorphic ASIC for interoperability across multi-mode H.264 decoders

Run-time interoperability between different applications based on H.264/AVC is an emerging need in networked infotainment, where media delivery must match the desired resolution and quality of the end terminals. In this paper, we describe the architecture and design of a polymorphic ASIC to support this. The H.264 decoding flow is partitioned into modules, such that the polymorphic ASIC meets the design goals of low-power, low-area, high flexibility, high throughput and fast interoperability between different profiles and levels of H.264. We demonstrate the idea with a multi-mode decoder that can decode baseline, main and high profile H.264 streams and can interoperate at run.time across these profiles. The decoder is capable of processing frame sizes of up to 1024 times 768 at 30 fps. The design synthesized with UMC 0.13 mum technology, occupies 250 k gates and runs at 100 MHz.

Area Optimized H.264 Intra Prediction Architecture for 1080p HD Resolution

High performance video standards use prediction techniques to achieve high picture quality at low bit rates. The type of prediction decides the bit rates and the image quality. Intra Prediction achieves high video quality with significant reduction in bit rate. This paper present an area optimized architecture for Intra prediction, for H.264 decoding at HDTV resolution with a target of achieving 60 fps. The architecture was validated on Virtex-5 FPGA based platform. The architecture achieves a frame rate of 64 fps. The architecture is based on multi-level memory hierarchy to reduce latency and ensure optimum resources utilization. It removes redundancy by reusing same functional blocks across different modes. The proposed architecture uses only 13% of the total LUTs available on the Xilinx FPGA XC5VLX50T.

HD resolution intra prediction architecture for H.264 decoder

High performance video standards use prediction techniques to achieve high picture quality at low bit rates. The type of prediction decides the bit rates and the image quality. Intra Prediction achieves high video quality with significant reduction in bit rate. This paper presents novel area optimized architecture for Intra prediction of H.264 decoding at HDTV resolution. The architecture has been validated on a Xilinx Virtex-5 FPGA based platform and achieved a frame rate of 64 fps. The architecture is based on multi-level memory hierarchy to reduce latency and ensure optimum resources utilization. It removes redundancy by reusing same functional blocks across different modes. The proposed architecture uses only 13% of the total LUTs available on the Xilinx FPGA XC5VLX50T.

High speed digital holographic interferometry for hypersonic flow visualization

Optical imaging techniques have played a major role in understanding the flow dynamics of varieties of fluid flows, particularly in the study of hypersonic flows. Schlieren and shadowgraph techniques have been the flow diagnostic tools for the investigation of compressible flows since more than a century. However these techniques provide only the qualitative information about the flow field. Other optical techniques such as holographic interferometry and laser induced fluorescence (LIF) have been used extensively for extracting quantitative information about the high speed flows. In this paper we present the application of digital holographic interferometry (DHI) technique integrated with short duration hypersonic shock tunnel facility having 1 ms test time, for quantitative flow visualization. Dynamics of the flow fields in hypersonic/supersonic speeds around different test models is visualized with DHI using a high-speed digital camera (0.2 million fps). These visualization results are compared with schlieren visualization and CFD simulation results. Fringe analysis is carried out to estimate the density of the flow field.

RAPID HUMAN ACTION RECOGNITION IN H.264/AVC COMPRESSED DOMAIN FOR VIDEO SURVEILLANCE

This paper discusses a novel high-speed approach for human action recognition in H. 264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of our work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can effect in reduced hardware utilization and fast recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust in outdoor as well as indoor testing scenarios. We have tested our method on two benchmark action datasets and achieved more than 85% accuracy. The proposed algorithm classifies actions with speed (>2000 fps) approximately 100 times more than existing state-of-the-art pixel-domain algorithms.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.

High-throughput miniaturized microfluidic microscopy with radially parallelized channel geometry

In this article, we present a novel approach to throughput enhancement in miniaturized microfluidic microscopy systems. Using the presented approach, we demonstrate an inexpensive yet high-throughput analytical instrument. Using the high-throughput analytical instrument, we have been able to achieve about 125,880 cells per minute (more than one hundred and twenty five thousand cells per minute), even while employing cost-effective low frame rate cameras (120 fps). The throughput achieved here is a notable progression in the field of diagnostics as it enables rapid quantitative testing and analysis. We demonstrate the applicability of the instrument to point-of-care diagnostics, by performing blood cell counting. We report a comparative analysis between the counts (in cells per mu l) obtained from our instrument, with that of a commercially available hematology analyzer.