Wonha Kim
Kyung Hee University · 情報科学
研究室紹介
Professor Wonha Kim's research lab specializes in advanced image and video processing, with a strong focus on perceptual quality optimization, efficient signal representation, and robust coding techniques. The lab explores innovative methods in dehazing, subband and wavelet-based coding, radial image processing, and entropy-constrained quantization, all aimed at enhancing visual fidelity while minimizing computational complexity. Key research directions include perceptual video coding, geometric image modeling, and joint optimization of transform, quantization, and entropy coding for high-performance image and video compression. The lab emphasizes the integration of human visual perception with signal processing algorithms to achieve superior visual quality under constrained bitrates and implementation resources.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15In this paper, we propose a new single-image dehazing method. The proposed method constructs color ellipsoids that are statistically fitted to haze pixel clusters in RGB space and then calculates the transmission values through color ellipsoid geometry. The transmission values generated by the proposed method maximize the contrast of dehazed pixels, while preventing over-saturated pixels. The values are also statistically robust because they are calculated from the averages of the haze pixel val
We have developed a video processing method that achieves human perceptual visual quality-oriented video coding. The patterns of moving objects are modeled by considering the limited human capacity for spatial-temporal resolution and the visual sensory memory together, and an online moving pattern classifier is devised by using the Hedge algorithm. The moving pattern classifier is embedded in the existing visual saliency with the purpose of providing a human perceptual video quality saliency mod
This paper presents an algorithm that jointly optimizes a lattice vector quantizer (LVQ) and an entropy coder in a subband coding at all ranges of bit rate. Estimation formulas for both entropy and distortion of lattice quantized subband images are derived. From these estimates, we then develop dynamic algorithm optimizing the LVQ and entropy coder together for a given entropy rate. Compared to previously reported min-max approaches, or approaches using asymptotic distortion bounds, the approach
In this work, wavelet basis and source coding are jointly optimized, while specifying the source coding strategy as entropy-constrained lattice vector quantizer (ECLVQ). The presented approach differs from previous works in which the choice of wavelet basis is quasioptimal, but the quantizer set is optimally chosen.
This paper proposes a radial image processing method performed in an L1-norm-based discrete polar coordinate system. For this purpose, we address the problem that polar coordinates based on the L2-norm cannot exist in discrete systems and then develop a method for converting Cartesian coordinates to L1-norm-based discrete polar coordinates. The proposed method greatly reduces the directional variance occurring in the Cartesian coordinate system and so processes radial directional images along th
Proposes an algorithm to use in designing a subband coder (SBC) constructed by wavelet packet, to achieve minimum distortion for a given bit budget and implementation complexity. The authors map the QMF tree structures onto a binary tree, then formulate the task as an optimization problem including coding bit and implementation complexity constraints. The problem is dissected into two phases. First, they derive the optimal bit allocation strategy which covers the entire range of bit rate, and se
This paper develops a detail image signal enhancement that makes images perceived as being clearer and more resolved and so more effective for higher resolution displays. We observe that the local variant signal enhancement makes images more vivid, and the more revealed granular signals harmonically embedded on the local variant signals make images more resolved. Based on this observation, we develop a method that not only emphasizes the local variant signals by scaling up the frequency energy i
In the decoding process associated with interframe wavelet coding, the inverse wavelet transform requires high computational complexity. However, as video technology starts to pervade all aspects of our lives, decoders are becoming required in various devices such as PDAs, notebooks, PCs, and set-top boxes. Therefore, a decoder's complexity needs to be adapted to the processor's computational power, and consequently a low-complexity codec is also required for scalable video coding. In this paper
In the decoding process associated with interframe wavelet coding, the inverse wavelet transform requires high computational complexity. However, as video technology starts to pervade all aspects of our lives, decoders are becoming required in various devices such as PDAs, notebooks, PCs, and set-top boxes. Therefore, a decoder's complexity needs to be adapted to the processor's computational power, and consequently a lowcomplexity codec is also required for scalable video coding. In this paper,
This paper presents an algorithm that jointly optimizes a lattice vector quantizer (LVQ) and an entropy coder in a subband coding at all ranges of bit rate. Estimation formulas for both entropy and distortion of lattice quantized subband images are derived. From these estimates, we then develop dynamic algorithm optimizing the LVQ and entropy coder together for a given entropy rate. Compared to previously reported min-max approaches, or approaches using asymptotic distortion bounds, the approach
We present a new approach for generating thumbnail images from H.264/AVC coded bit streams. We have verified analytically that mismatch errors between the encoder and decoder prevent the direct generation of thumbnail images from H.264/AVC transform coefficients. Based on this analysis, we have devised a method that exploits both the spatial and transform domains. What distinguishes our algorithm from previous works is that it determines the thumbnail image pixels by summing the residual and est