Tag: Statistics


CAVLC Encoder Design for Real-Time Mobile Video Applications


This brief presents a new context-based adaptive variable length coding (CAVLC) architecture. The prototype is designed for the H.264/AVC baseline profile entropy coder. The proposed design offers area savings by reducing the size of the statistic buffer. The arithmetic table elimination technique further reduces the area. The split VLC tables simplify the process of bit-stream generation and also help in reducing some area. The proposed architecture is implemented on Xilinx Virtex II field-programmable gate array (2v3000fg676-4). Simulation result shows that the architecture is capable of processing common/quarter-common intermediate format frame sequences in real-time at a core speed of 50 MHz with 6.85-K logic gates.

Published in:

Circuits and Systems II: Express Briefs, IEEE Transactions on (Volume:54 , Issue: 10 )

C. A. Rahman and W. Badawy, “CAVLC Encoder Design for Real-time Mobile Video Applications”, The IEEE Trans. on Circuits and Systems II, Oct. 2007 Vol 54, Issue: 10, pp. 873-877.
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High-Throughput Identification and Classification Algorithm for Leukemia Population Statistics

Early detection of leukemia and reduced risk to human health can result from interdisciplinary integration of image analysis with clinical experimental results. Image analysis relies on efficient and reliable processing algorithms to make quantitative judgments on image data. This article presents the design and implementation of an efficient and high-throughput leukemia cell count and cluster classification algorithm to automatically quantify leukemia population statistics in the field of view. The algorithm is divided into two stages: (1) the cell identification stage and (2) the cell classification and inspection stage. The cell identification stage accurately segments background and noise from foreground pixels. A boundary box is generated enclosing the foreground pixels identifying all isolated cells and cell clusters. The cell classification and inspection stage uses one-dimensional intensity profiles that behave as signature plots to segregate isolated cells from cell clusters and evaluate total count within each cluster. The designed algorithm is tested with a variety of leukemia cell images that vary in image acquisition conditions, image sizes, cell sizes, intensity distributions, and image quality. The proposed algorithm demonstrates good potential in processing both ideal and nonideal images with an average accuracy of 91% and average processing time of 3 s. The performance of the proposed algorithm in comparison to recently published algorithms and commercial image analysis tool further ascertains its robustness.

Brinda Prasad and Wael Badawy, “High-Throughput Identification and Classification Algorithm for Leukemia Population Statistics,” The Journal of Imaging Science and Technology 52(3), 2008.

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