• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.7 s.361
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• pp.45-53
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• 2007

In this paper, we propose an efficient hardware architecture for H.264/AVC CAVLC (Context-based Adaptive Variable Length Coding) encoding. Previous CAVLC architectures search all of the coefficients to find statistic characteristics in a block. However, it is unnecessary information that zero coefficients following the last position of a non-zero coefficient when CAVLC encodes residual coefficients. In order to reduce this unnecessary operation, we propose two techniques, which detect the first and last position of non-zero coefficients and arrange non-zero coefficients sequentially. By adopting these two techniques, the required processing time was reduced about 23% compared with previous architecture. It was designed in a hardware description language and total logic gate count is 16.3k using 0.18um standard cell library Simulation results show that our design is capable of real-time processing for $1920{\times}1088\;30fps$ videos at 81MHz.

• Smart Structures and Systems
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• v.31 no.4
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• pp.335-349
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• 2023

Bridges constantly undergo deterioration and damage, the most common ones being concrete damage and exposed rebar. Periodic inspection of bridges to identify damages can aid in their quick remediation. Likewise, identifying components can provide context for damage assessment and help gauge a bridge's state of interaction with its surroundings. Current inspection techniques rely on manual site visits, which can be time-consuming and costly. More recently, robotic inspection assisted by autonomous data analytics based on Computer Vision (CV) and Artificial Intelligence (AI) has been viewed as a suitable alternative to manual inspection because of its efficiency and accuracy. To aid research in this avenue, this study performs a comparative assessment of different architectures, loss functions, and ensembling strategies for the autonomous segmentation of bridge components and damages. The experiments lead to several interesting discoveries. Nested Reg-UNet architecture is found to outperform five other state-of-the-art architectures in both damage and component segmentation tasks. The architecture is built by combining a Nested UNet style dense configuration with a pretrained RegNet encoder. In terms of the mean Intersection over Union (mIoU) metric, the Nested Reg-UNet architecture provides an improvement of 2.86% on the damage segmentation task and 1.66% on the component segmentation task compared to the state-of-the-art UNet architecture. Furthermore, it is demonstrated that incorporating the Lovasz-Softmax loss function to counter class imbalance can boost performance by 3.44% in the component segmentation task over the most employed alternative, weighted Cross Entropy (wCE). Finally, weighted softmax ensembling is found to be quite effective when used synchronously with the Nested Reg-UNet architecture by providing mIoU improvement of 0.74% in the component segmentation task and 1.14% in the damage segmentation task over a single-architecture baseline. Overall, the best mIoU of 92.50% for the component segmentation task and 84.19% for the damage segmentation task validate the feasibility of these techniques for autonomous bridge component and damage segmentation using RGB images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.2C
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• pp.181-188
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• 2007

This paper proposed a watermark embedding/extracting method using CABAC(Context-based Adaptive Binary Arithmetic Coding) which is the entropy encoder for the main profile of MPEG-4 Part 10 H.264/AVC. This algorithm selects the blocks and the coefficients in a block on the bases of the contexts extracted from the relationship to the adjacent blocks and coefficients. A watermark bit is embedded without any modification of coefficient or with replacing the LSB(Least Significant Bit) of the coefficient with a watermark bit by considering both the absolute value of the selected coefficient and the watermark bit. Therefore, it makes it hard for an attacker to find out the watermarked locations. By selecting a few coefficients near the DC coefficient according to the contexts, this algorithm satisfies the robustness requirement. From the results from experiments with various kinds and various strengths of attacks the maximum error ratio of the extracted watermark was 5.02% in maximum, which makes certain that the proposed algorithm has very high level of robustness. Because it embeds the watermark during the context modeling and binarization process of CABAC, the additional amount of calculation for locating and selecting the coefficients to embed watermark is very small. Consequently, it is highly expected that it is very useful in the application area that the video must be compressed right after acquisition.