O
Oscar C. Au
Researcher at Hong Kong University of Science and Technology
Publications - 493
Citations - 7851
Oscar C. Au is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: Motion estimation & Motion compensation. The author has an hindex of 40, co-authored 491 publications receiving 7493 citations. Previous affiliations of Oscar C. Au include Wilmington University & Huawei.
Papers
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Patent
Methods and apparatus for hiding data in halftone images
Oscar C. Au,Ming Sun Fu +1 more
TL;DR: In this paper, the authors proposed methods for generating a halftone image, in which each pixel takes one of two tone values, which is present at data storage pixels chosen using a pseudo-random number generator.
Journal ArticleDOI
Edge-Directed Error Concealment
TL;DR: Simulation results show that compared to the existing boundary matching algorithm and the exemplar-based inpainting approach, the proposed EDEC algorithm can reconstruct the corrupted frame with both a better visual quality and a higher decoder peak signal-to-noise ratio.
Proceedings ArticleDOI
Data hiding by smart pair toggling for halftone images
Ming Sun Fu,Oscar C. Au +1 more
TL;DR: Simulation results suggest that the proposed data hiding by smart pair-toggling (DHSPT) algorithm can hide the same amount of data while generating halftone images with considerably better visual quality than DHPT.
Patent
Device, method and digital video encoder for block-matching motion estimation
TL;DR: In this paper, a method for video compression uses a technique in which changes in the image are encoded by motions of block of the image and signals indicating evolutions in the block.
Proceedings ArticleDOI
New results on zonal based motion estimation algorithms-advanced predictive diamond zonal search
TL;DR: This paper proposes a further improvement on these algorithms named the Advanced Predictive Diamond Zonal Search (APDZS), which introduces the concepts of multiple initial predictor candidates and adaptive thresholding, and manages to significantly improve the reliability and performance of the estimation.