Cheonsu Park
Sungkyunkwan University · 情報科学
研究室紹介
Professor Cheonsu Park's research lab specializes in digital signal processing, with a strong focus on efficient and accurate transform algorithms for multimedia signal compression and analysis. The lab investigates advanced video coding techniques, particularly in depth video coding and multiview video compression, aiming to reduce computational complexity while maintaining high compression efficiency. Additionally, the lab explores image forensics and authentication, emphasizing tamper detection and the retrieval of original source images from spliced fakes using hybrid feature extraction methods. Their work bridges theoretical signal processing with practical applications in computer vision, image processing, and multimedia security.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15The 3D video extension of High Efficiency Video Coding (3D-HEVC) is the state-of-the-art video coding standard for the compression of the multiview video plus depth format. In the 3D-HEVC design, new depth-modeling modes (DMMs) are utilized together with the existing intraprediction modes for depth intracoding. The DMMs can provide more accurate prediction signals and thereby achieve better compression efficiency. However, testing the DMMs in the intramode decision process causes a drastic incre
Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale
The discrete Fourier transform (DFT) produces a Fourier representation for finite-duration data sequences. In addition to its theoretical importance, the DFT plays a key role in the implementation of a variety of digital signal-?processing algorithms. Several algorithms including the fast Fourier transform (FFT) and the Goertzel algorithm have been introduced for the fast implementation of the DFT [1], [2].
Discrete Fourier transform (DFT) is the most widely used method for determining the frequency spectra of digital signals. In this paper, a 2D sliding DFT (2D SDFT) algorithm is proposed for fast implementation of the DFT on 2D sliding windows. The proposed 2D SDFT algorithm directly computes the DFT bins of the current window using the precalculated bins of the previous window. Since the proposed algorithm is designed to accelerate the sliding transform process of a 2D input signal, it can be di
Discrete orthogonal transforms such as the discrete Fourier transform (DFT), discrete Hartley transform (DHT), and Walsh?Hadamard transform (WHT) play important roles in the fields of digital signal processing, filtering, and communications. In recent years, there has been a growing interest in the sliding transform process where the transform window is shifted one sample at a time and the transform process is repeated.
The discrete Fourier transform (DFT) is the most widely used technique for determining the frequency spectra of digital signals. However, in the sliding transform scenario where the transform window is shifted one sample at a time and the transform process is repeated, the use of DFT becomes difficult due to its heavy computational burden. This paper proposes an optimal sliding DFT (oSDFT) algorithm that achieves both the lowest computational requirement and the highest computational accuracy am
The scalable video coding (SVC) standard adopts the inter-layer residual prediction (ILRP) algorithm to encode the residual signal of the enhancement layer (EL). The ILRP reduces the number of bits required for encoding the residual signal but incurs excessive encoding time. In this paper, we propose a fast encoding algorithm for SVC-based video streaming. In this algorithm, the ILRP is selectively applied to the coding modes depending on their Lagrangian rate-distortion costs. Experimental resu
The three‐dimensional (3D) extension of the high‐efficiency video coding (3D‐HEVC) standard adopts new depth‐modelling modes (DMMs) to provide an alternative prediction scheme for depth‐map intra‐coding. In 3D‐HEVC, although edges in depth maps can be accurately estimated by utilising the DMMs, testing the DMMs in the mode decision introduces a huge computational load to the encoder. A fast mode decision algorithm is proposed that can significantly reduce the computational overhead incurred by t
In the image forgery problems, previous works has been chiefly designed considering only one of two forgery types: copy-move and splicing. In this paper, we propose a scheme to handle both copy-move and splicing image forgery by concurrently classifying the image forgery types and localizing the forged regions. The structural correlations between images are employed in the forgery clustering algorithm to assemble relevant images into clusters. Then, we search for the matching of image regions in
Recently, there is an active research about a new emerging video coding standard, scalable video coding (SVC). SVC adopts a layered coding scheme to generate multi-layered bitstream for heterogeneous environments. In addition to the layered coding, SVC performs temporal prediction using the hierarchical B picture scheme in order to provide temporal scalable representation of input sequences. Since the coding order of the hierarchical B picture scheme is different from that of conventional tempor