Browsing by Author "Hsu, Po-Hsiung"

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A 124 Mpixels/s VLSI Design for Histogram-Based Joint Bilateral Filtering
(2011-11-01)
Tseng, Yu-Cheng
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Hsu, Po-Hsiung
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Chang, Tian-Sheuan
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
This paper presents an efficient and scalable design for histogram-based bilateral filtering (BF) and joint BF (JBF) by memory reduction methods and architecture design techniques to solve the problems of high memory cost, high computational complexity, high bandwidth, and large range table. The presented memory reduction methods exploit the progressive computing characteristics to reduce the memory cost to 0.003%-0.020%, as compared with the original approach. Furthermore, the architecture design techniques adopt range domain parallelism and take advantage of the computing order and the numerical properties to solve the complexity, bandwidth, and range-table problems. The example design with a 90-nm complementary metal-oxide-semiconductor process can deliver the throughput to 124 Mpixels/s with 356-K gate counts and 23-KB on-chip memory.
Low Memory Cost Bilateral Filtering Using Stripe-based Sliding Integral Histogram
(2010)
Hsu, Po-Hsiung
;
Tseng, Yu-Cheng
;
Chang, Tian-Sheuan
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Bilateral filter can well smooth images while preserving edges, and thus has been widely used in various applications. However, it needs significant memory cost due to a whole image storage. This paper develops three methods to significantly reduce the memory cost. The runtime updating method (RUM) discards unnecessary data in runtime. The stripe-based integral histogram method (SBM) divides image into vertical image-stripes. The sliding origin method (SOM) sliding moves the origin of integration region to achieve the most memory reduction. The final result shows that the proposed approach only needs 24Kbits memory, which saves 99.993% of memory cost for smoothing a VGA-sized image with 30-fps speed, when compared to the previous optimized constant time integral histogram approach.