Guan Ju Peng

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Papers by Guan Ju Peng

Optimizing Quantum Reversible Circuits Using Reinforcement Learning

Learning the sparse prior: Modern approaches

Wiley interdisciplinary reviews. Computational statistics, 2024

Predicting flap failure in head and neck surgery: Data Augmentation and Resampling for Data Imbalance

Objective The area under receiver operating characteristic curve (AUROC) is hampered by imbalance... more Objective The area under receiver operating characteristic curve (AUROC) is hampered by imbalanced data using artificial intelligent models for predicting free flap failure. Thus, we propose a new data preprocessing method with Gaussian Noise and Up-Sampling to increase the AUROC score. Study design: Case-control study Setting: Data were obtained from patients with head and neck cancer who underwent free flap reconstruction at Changhua Christian Hospital in Taiwan between May 2019 and June 2020. Methods The collected dataset was initially divided into training and validation sets. Subsequently, a data augmentation technique was employed on the training dataset to generate additional training data, thereby addressing the issue of imbalanced sample sizes between successful and unsuccessful outcomes. The rebalanced training data are then utilized to optimize the parameters of diverse machine learning frameworks, including logistic regression(LR), random forest (RF), support vector mach...

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Optimal Rate Allocation for Scalable Video Coding

我們在可伸縮視頻編碼中考慮了每個使用者對於不同解析度的偏好。並且根據這些用戶的偏好，我們制定並解決基於小波變換的可伸縮視頻編碼和 H.264/SVC 的位元分配問題。首先我們考慮基於小波變換的... more 我們在可伸縮視頻編碼中考慮了每個使用者對於不同解析度的偏好。並且根據這些用戶的偏好，我們制定並解決基於小波變換的可伸縮視頻編碼和 H.264/SVC 的位元分配問題。首先我們考慮基於小波變換的視頻編碼器的位元分配方法。我們提出了三種方法來解決位元分配的問題。首先是使用拉格朗日的高效率方法來解決優化問題上限，其二是效率較低的動態規劃法，但其可以得到問題的最佳解。這兩種方法都需要先了解用戶的喜好。對於未知用戶喜好的情況下，我們用最小化最大失真法來解決問題。我們發現，最糟糕的表現往往發生在所有的用戶都訂閱相同的解析度的時候。因此，最小化最大失真法與小波編解碼器傳統的位元分配方法相同。根據我們進行多次的實驗，這些實驗分別考量了各種用戶的偏好，結果表明，了解用戶的喜好顯著提高的可伸縮視頻編解碼器的編碼性能。H.264/SVC 的位元分配問題要複雜得多，我們必須了解並分析H.264/SVC 多層編碼結構造成的失真。在這篇論文中，我們具體地分析了用於實現時間 (temporal)，空間(spatial)和質量(quality)的可伸縮視頻編碼（SVC）的編碼結構，並且根據分析的結果，提出了兩個對於失真-碼率線（R-D Curve）的優化算法：其一是已知用戶偏好的最優化演算法，另一個是最小化最大失真法。與目前最先進的位元分配法相較，當用戶的偏好都是已知的時候，我們的演算法在壓縮效率上有著顯著的改進。這篇論文中，我們對可伸縮視頻編碼提出了用戶偏好的概念，並在兩個最常見的可伸縮性視頻編碼方法中解決相應的位元分配問題。它們分別是，MCTF-EZBC 基於小波編碼器和H.264/SVC。在比較已知使用者偏好方法與未知使用者偏好方法的效能之後，我們亦驗證了可伸縮視頻編碼中用戶偏好的重要性。The scalable video coding problem is investigated, and based on the preferred resolution, the bit allocation problems of wavelet-based scalable video coding and H.264/SVC are formulated and solved. For the wavelet-based video encoder, three me...

Wireless Video Bitrate Control Queuing model

中文摘要隨著無線網路的普及，透過無線網路的視訊傳輸變成非常重要的課題。相較於有線網路，無線網路的資料傳輸較不穩定，頻寬較小且更容易隨著使用者所處的環境而變化。所以無線網路對於影像傳輸而言，無疑... more 中文摘要隨著無線網路的普及，透過無線網路的視訊傳輸變成非常重要的課題。相較於有線網路，無線網路的資料傳輸較不穩定，頻寬較小且更容易隨著使用者所處的環境而變化。所以無線網路對於影像傳輸而言，無疑地是更為嚴苛的傳輸環境。因此，對於期望維持品質的視訊傳輸而言，必須要提供能夠隨著頻寬的波動而改變影像壓縮碼率的方法，以期能夠有效利用頻寬，而達到影像品質最佳化、減少延遲以及增強畫面平滑性的效果。在本論文中，我們結合了排隊理論、H.264/AVC以及相對應的碼率控制機制，提出無線視訊碼率控制之排隊模型。目的在於針對不同的頻寬，能夠估計出在該頻寬下最適合之碼率。排隊理論用以描述視訊資料在編碼端暫存記憶體中的處裡情形，並據此結合目前頻寬估計出最適碼率。H.264/AVC在編碼效能上相較於之前的視訊壓縮標準有著顯著的進步，而在許多的即時視訊應用中，往往必須在極短的時間內處理龐大的資料。因此，如H.264/AVC一般，高效率的視訊壓縮技術是必須的。除此之外，針對H.264/AVC設計的碼率控制機制，讓實際碼率能夠確實的逼近由排隊理論估計出的最適視訊碼率。藉由這樣的設計，我們讓視訊品質、延遲以及視訊畫面平滑性同時獲得合理的控制，並且能夠符合即時視訊傳輸的標準。ABSTRACT With the prevailing of wireless communication, transmission of video applications through wireless network becomes an important issue. Comparing to wired network, the data transmission is more unstable and the bandwidth is less in wireless network. Thus, video transmission over wireless network becomes more challenging. Since available bandwidth during transmission varies frequently, in order to maintain the video quality and utilize the available bandwidt...

Fast Implementation of the Subband Weighting for 3D Wavelet Coding

ISRN Signal Processing, 2011

Performing optimal bit-allocation with 3D wavelet coding methods is difficult, because energy is ... more Performing optimal bit-allocation with 3D wavelet coding methods is difficult, because energy is not conserved after applying the motion-compensated temporal filtering (MCTF) process and the spatial wavelet transform. The problem has been solved by extending the 2D wavelet coefficients weighting method with the consideration of the complicated pixel connectivity resulting from the lifting-based MCTF process. However, the computation of weighting factors demands much computing time and vast amount of memory. In this paper, we propose a method, which facilitates the property of sparse matrices, to compute the subband weighing factors efficiently. The proposed method is employed for the 5–3 temporal filter and the 9–7 temporal filter in the lifting-based MCTF process. Experiments on various video sequences show that the computing time and memory needed for the computation of weighting factors are dramatically reduced by applying the proposed approach.

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Distortion estimation and bit allocation for MCTF based 3-D wavelet video coding

2008 15th IEEE International Conference on Image Processing, 2008

In this paper, we propose a novel way to derive the weighting for subbands that conserves energy.... more In this paper, we propose a novel way to derive the weighting for subbands that conserves energy. Once the weight of each subband has been calculated by motion-compensation temporal filtering, the effect of the quantization error of a subband on the reconstruction error of a group of pictures can be determined. Then, we weigh each subband and use the weighted

Signal Separation Using Re-Weighted and Adaptive Morphological Component Analysis

Morphological component analysis (MCA) for signal separation decomposes a signal into a superposi... more Morphological component analysis (MCA) for signal separation decomposes a signal into a superposition of morphological subcomponents, each of which is approximately sparse in a certain dictionary. Some of the dictionaries can also be modified to make them adaptive to local structure in images. We show that signal separation performance can be improved over the previous MCA approaches by replacing L1 norm optimization with “weighted” L1 norm optimization and replacing their dictionary adaptation with regularized dictionary adaptation. The weight on an atom for sparse coding is commonly derived from the corresponding coefficient’s value. In contrast, the weight of an atom in a dictionary for signal separation is derived from the mutual coherence between the atom and the atoms in the other dictionaries. The proposed solution for regularized dictionary adaptation is an extension of the K-SVD method, where the dictionary and “weighted” sparse coefficients are estimated simultaneously. We present a series of experiments demonstrating the significant performance improvement of the proposed algorithm over the previous approaches for signal separation.

Optimal Bit-Allocation for Wavelet Scalable Video Coding with User Preference

Advanced Video Coding for Next-Generation Multimedia Services, 2013

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Optimal Bit-allocation for Wavelet-based Scalable Video Coding

2012 IEEE International Conference on Multimedia and Expo, 2012

ABSTRACT We investigate the wavelet-based scalable video coding problem and present a solution th... more ABSTRACT We investigate the wavelet-based scalable video coding problem and present a solution that takes account of each user&#39;s preferred resolution. Based on the preference, we formulate the bit allocation problem of wavelet-based scalable video coding. We propose three methods to solve the problem. The first is an efficient Lagrangian-based method that solves the upper bound of the problem optimally, and the second is a less efficient dynamic programming method that solves the problem optimally. Both methods require knowledge of the user&#39;s preference. For the case where the user&#39;s preference is unknown, we solve the problem by a min-max approach. Our objective is to find the bit allocation solution that maximizes the worst possible performance. We show that the worst performance occurs when all users subscribe to the same spatial, temporal, and quality resolutions. Thus, the min-max solution is exactly the same as the traditional bit allocation method for a non-scalable wavelet codec. We conduct several experiments on the 2D+t MCTF-EZBC wavelet codec with respect to various subscribers&#39; preferences. The results demonstrate that knowing the users&#39; preferences improves the coding performance of the scalable video codec significantly.

Interlayer Bit Allocation for Scalable Video Coding

IEEE Transactions on Image Processing, 2012

In this paper, we present a theoretical analysis of the distortion in multi-layer coding structur... more In this paper, we present a theoretical analysis of the distortion in multi-layer coding structures. Specifically, we analyze the prediction structure used to achieve temporal, spatial, and quality scalability of scalable video coding (SVC), and show that the average peak-signal-to-noise (PSNR) of SVC is a weighted combination of the bit rates assigned to all the streams. Our analysis utilizes the end user's preference for certain resolutions. We also propose a rate-distortion (R-D) optimization algorithm, and compare its performance with that of a state-of-the-art scalable bit allocation algorithm. The reported experiment results demonstrate that the R-D algorithm significantly outperforms the compared approach in terms of the average PSNR.

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A Proximal Method for Convolutional Dictionary Learning with Convergence Property

2018 26th European Signal Processing Conference (EUSIPCO), 2018

The convolutional sparse coding (CSC) is superior in representing signals, and to obtain the best... more The convolutional sparse coding (CSC) is superior in representing signals, and to obtain the best performance of CSC, the dictionary is usually learned from data. The so-called convolution dictionary learning (CDL) problem is therefore formulated for the purpose. Most of the solvers for CDL alternately update the coefficients and dictionary in an iterative manner, and as a consequence, numerous redundant iterations incur slow speed in achieving convergence. Moreover, their convergence properties can hardly be analyzed even though the $\ell_{0}$ sparse inducing function is approximated by the convex $\ell_{0}$ norm. In this article, we propose an algorithm which directly deals with the non-convex non-smooth $\ell_{0}$ constraint and provides a sound convergence property. The experimental results show that, the proposed method achieves the convergence point with less time and a smaller final function value compared to the existing convolutional dictionary learning algorithms.

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Subband Weighting With Pixel Connectivity for 3-D Wavelet Coding

IEEE Transactions on Image Processing, 2009

Performing optimal bit-allocation with 3-D wavelet coding methods is difficult because energy is ... more Performing optimal bit-allocation with 3-D wavelet coding methods is difficult because energy is not conserved after applying the motion-compensated temporal filtering (MCTF) process and the spatial wavelet transform. The problem cannot be solved by extending the 2-D wavelet coefficients weighting method directly and then applying the result to 3-D wavelet coefficients, since this approach does not consider the complicated pixel connectivity that results from the lifting-based MCTF process. In this paper, we propose a novel weighting method, which takes account of the pixel connectivity, to solve the problem and derive the effect of the quantization error of a subband on the reconstruction error of a group of pictures. We employ the proposed method on a 2-D + t structure with different temporal filters, namely the 5-3 filter and the 9-7 filter. Experiments on various coding parameters and sequences show that the proposed approach improves the bit-allocation performance over that obtained by using the weightings derived without considering the pixel connectivity in the MCTF process.

Multi-Objective Optimization and Characterization of Pareto Points for Scalable Coding

IEEE Transactions on Circuits and Systems for Video Technology

Joint and Direct Optimization for Dictionary Learning in Convolutional Sparse Representation

IEEE Transactions on Neural Networks and Learning Systems

Adaptive ADMM for Dictionary Learning in Convolutional Sparse Representation

IEEE Transactions on Image Processing

Rate-allocation for spatially scalable video coding

in all layers as a weighted average of the distortions of the layers, and then use R-D optimizati... more in all layers as a weighted average of the distortions of the layers, and then use R-D optimization based on the Lagrangian technique to optimize the weighted distortion. In [5], Schwartz and Wiegand propose an encoder control mechanism that jointly optimizes the coding parameters of the base layer and enhancement layers under H.264/SVC. Their algorithm also utilizes a weighted combination

A Proximal Method for Dictionary Updating in Sparse Representations

IEEE Transactions on Signal Processing, 2015

ABSTRACT In this paper, we propose a new dictionary updating method for sparse dictionary learnin... more ABSTRACT In this paper, we propose a new dictionary updating method for sparse dictionary learning. Our method imposes the norm constraint on coefficients as well as a proximity regularization on the distance of dictionary modifications in the dictionary updating process. We show that the derived dictionary updating rule is a generalization of the K-SVD method. We study the convergence and the complexity of the proposed method. We also compare its performance with that of other methods.

A proximal method for the K-SVD dictionary learning

2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013

ABSTRACT In this paper, we propose a dictionary updating method and show numerically that it can ... more ABSTRACT In this paper, we propose a dictionary updating method and show numerically that it can converge to a dictionary that outperforms the dictionary derived by the K-SVD method. The proposed method is based on the proximal point approach used in the convex optimization algorithm. We incorporate the approach into the well-known MOD and combine the result with the K-SVD method to obtain the proposed method. We analyze the complexity of the proposed method and compare it with that of the K-SVD method. The results of experiments demonstrate that our method outperforms K-SVD with only a slight increase in the execution time.

AND-OR tree-based mode selection for video coding

Signal, Image and Video Processing, 2011

The conventional video coding approach is pragmatic in that researchers design different methods ... more The conventional video coding approach is pragmatic in that researchers design different methods and compare their results to state-of-the-art methods. Although the H.264/AVC baseline is effective, there is a need to develop an abstract view of video coding, in the hope that new insights can be derived from the abstraction. In this paper, we propose a preliminary approach based on an AND-OR tree representation of video coding. We show that the H.264/AVC baseline can be represented as an AND-OR tree structure. Based on the AND-OR tree representation, we propose two video coding systems: one is a T + 2D wavelet codec based on a motion-compensated temporal filtering (MCTF) lifting structure, and the other is the AND-OR tree implementation of the H.264/AVC baseline. We also compare the proposed systems' coding performance in terms of the PSNR with that of H.264/AVC JM 16.2.

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