Byung Tae Oh

Depth Map Coding Based on Synthesized View Distortion Function

This paper presents an efficient depth map coding method based on a newly defined rendering view distortion function. As compared to the conventional depth map coding in which distortion is measured by only investigating the coding error in depth map, the proposed scheme focuses on virtually synthesized view quality by involving co-located color information. In detail, the proposed distortion function estimates rendered view quality, where area-based scheme is provided in order to mimic the warping/view-rendering process accurately. Moreover, the coding performance of the proposed distortion metric is even improved by involving the additional SKIP mode derived by co-located color coding information. The simulation results show the proposed scheme could achieve approximately 30% bit-rate saving for depth data, and about 10% bit-rate saving for overall multi-view data.

Fast Non-Local Means (NLM) Computation With Probabilistic Early Termination

A speed up technique for the non-local means (NLM) image denoising algorithm based on probabilistic early termination (PET) is proposed. A significant amount of computation in the NLM scheme is dedicated to the distortion calculation between pixel neighborhoods. The proposed PET scheme adopts a probability model to achieve early termination. Specifically, the distortion computation can be terminated and the corresponding contributing pixel can be rejected earlier, if the expected distortion value is too high to be of significance in weighted averaging. Performance comparative with several fast NLM schemes is provided to demonstrate the effectiveness of the proposed algorithm.

Improved image denoising with adaptive nonlocal means (ANL-means) algorithm

An adaptive nonlocal-means (ANL-means) algorithm for image denoising is proposed in this work. It employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique to achieve robust block classification in noisy images. Then, a local window is adaptively adjusted to match the local property of a block and a rotated matching algorithm that aligns the dominant orientation of a local region is adopted for similarity matching. Furthermore, the noise level is estimated using the block classification result and the Laplacian operator. Experimental results are given to demonstrate the superior denoising performance of the proposed ANL-means denoising technique over various image denoising benchmarks in terms of the PSNR value and perceptual quality comparison, where images corrupted by additive white Gaussian noise (AWGN) are tested.

Advanced Film Grain Noise Extraction and Synthesis for High-Definition Video Coding

A technique for film grain noise extraction, modeling and synthesis is studied and applied to high-definition video coding in this paper. Film grain noise enhances the natural appearance of pictures in high-definition video and should be preserved in coded video. However, the coding of video contents with film grain noise is expensive. In previous art, it was proposed to enhance the coding performance by extracting film grain noise from the input video at the encoder as a preprocessing step, and by resynthesizing and adding it back to the decoded video at the decoder as a postprocessing step. In a novel implementation of this approach, we first remove film grain noise from image/video with a variational denoising approach without distorting its original content. Then, we present a parametric model (consisting of a small set of parameters) to generate film grain noise that is close to the actual one in terms of a couple of observed statistical properties, such as the power spectral density and the crosschannel spectral correlation. Under this framework, the coding gain of denoised video is higher while the visual quality of the final reconstructed video is well preserved. Experimental results are provided to demonstrate the superior performance of the proposed approach.

Plane segmentation based intra prediction for depth map coding

This paper presents an efficient intra prediction scheme for depth map coding in multi-view plus depth (MVD) system. As compared to the conventional intra prediction algorithm, the proposed method segments the current block into k regions, and applies different prediction scheme for each segmented region, which results in higher prediction accuracy by avoiding wrong estimation across different regions. Moreover, we also provide the efficient lossless coding scheme for region segmentation information based on unique depth characteristics, by which the proposed scheme is competitive w.r.t. R-D cost. The simulation results show the superior performance of the proposed scheme comparing to the H.264/AVC intra prediction and other edge-based intra scheme for depth map.

Adaptive nonlocal means algorithm for image denoising

An adaptive image denoising technique based on the nonlocal means (NL-means) algorithm is investigated in this research. The proposed method first employs the singular value decomposition (SVD) method and the K-means clustering (K-means) technique for robust block classification in noisy images. Then, the local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching algorithm is adopted for better similarity matching. Experiment results are given to demonstrate the superior denoising performance of the proposed adaptive NL-means (ANL-means) denoising technique.

Synthesis-based texture coding for video compression with side information

This paper presents a synthesis-based texture coding technique that uses low-quality video as side information to control the output texture for video compression. As compared with the current pure synthesis algorithm, the proposed algorithm is generic, in the sense that the behavior and quality of the output texture can be adjusted by the amount of the side information and determined by the user. We develop an area-adaptive side information assignment technique to improve coding efficiency. Additionally, we present a fast-searching algorithm to reduce computational complexity. Simulations demonstrate the performance of the proposed technique.

Film grain noise modeling in advanced video coding

A new technique for film grain noise extraction, modeling and synthesis is proposed and applied to the coding of high definition video in this work. The film grain noise is viewed as a part of artistic presentation by people in the movie industry. On one hand, since the film grain noise can boost the natural appearance of pictures in high definition video, it should be preserved in high-fidelity video processing systems. On the other hand, video coding with film grain noise is expensive. It is desirable to extract film grain noise from the input video as a pre-processing step at the encoder and re-synthesize the film grain noise and add it back to the decoded video as a post-processing step at the decoder. Under this framework, the coding gain of the denoised video is higher while the quality of the final reconstructed video can still be well preserved. Following this idea, we present a method to remove film grain noise from image/video without distorting its original content. Besides, we describe a parametric model containing a small set of parameters to represent the extracted film grain noise. The proposed model generates the film grain noise that is close to the real one in terms of power spectral density and cross-channel spectral correlation. Experimental results are shown to demonstrate the efficiency of the proposed scheme.

Super-resolution texture synthesis using stochastic PAR/NL model

Super-resolution texture synthesis using a locally-adaptive stochastic signal model is investigated in this work. The 2D random texture is modeled by a piecewise auto-regressive (PAR) process whose parameters are determined by a non-local (NL) training procedure and, consequently, it is called the PAR/NL model. Unlike previous work that applies the NL scheme to image pixels directly, the proposed PAR/NL scheme applies the NL scheme to PAR model parameters by assuming that these parameters are self-similar. Furthermore, we describe a probabilistic method for PAR/NL model computation using the maximum a posteriori (MAP) and the expectation-maximization (EM) principles. Experimental results are given to demonstrate the synthesis performance of the proposed PAR/NL technique, which can boost texture detail and eliminate the blurring artifact perceptually.

Fast motion estimation algorithm for depth map

The paper presents a fast motion estimation scheme for depth map coding. The distortion measuring process has many redundant operations due to the piece-wise planner property of the depth map. The proposed scheme is designed to reduce those redundant operations by grouping each block into one or two representative values. Then, the entire pixel computations in a block are replaced with a few operations between representative values. As a result, the total number of operations is greatly reduced by 92%, while its impact on the performance is negligible regardless of the searching methods.