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1. On the Adversarial Robustness of Learning-based Image Compression Against Rate-Distortion Attacks

Author

Wu, Chenhao, Wu, Qingbo, Wei, Haoran, Chen, Shuai, Wang, Lei, Ngan, King Ngi, Meng, Fanman, and Li, Hongliang

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Despite demonstrating superior rate-distortion (RD) performance, learning-based image compression (LIC) algorithms have been found to be vulnerable to malicious perturbations in recent studies. However, the adversarial attacks considered in existing literature remain divergent from real-world scenarios, both in terms of the attack direction and bitrate. Additionally, existing methods focus solely on empirical observations of the model vulnerability, neglecting to identify the origin of it. These limitations hinder the comprehensive investigation and in-depth understanding of the adversarial robustness of LIC algorithms. To address the aforementioned issues, this paper considers the arbitrary nature of the attack direction and the uncontrollable compression ratio faced by adversaries, and presents two practical rate-distortion attack paradigms, i.e., Specific-ratio Rate-Distortion Attack (SRDA) and Agnostic-ratio Rate-Distortion Attack (ARDA). Using the performance variations as indicators, we evaluate the adversarial robustness of eight predominant LIC algorithms against diverse attacks. Furthermore, we propose two novel analytical tools for in-depth analysis, i.e., Entropy Causal Intervention and Layer-wise Distance Magnify Ratio, and reveal that hyperprior significantly increases the bitrate and Inverse Generalized Divisive Normalization (IGDN) significantly amplifies input perturbations when under attack. Lastly, we examine the efficacy of adversarial training and introduce the use of online updating for defense. By comparing their advantages and disadvantages, we provide a reference for constructing more robust LIC algorithms against the rate-distortion attacks.

Published
2024

2. Forgetting to Remember: A Scalable Incremental Learning Framework for Cross-Task Blind Image Quality Assessment

Author

Ma, Rui, Wu, Qingbo, Ngan, King Ngi, Li, Hongliang, Meng, Fanman, and Xu, Linfeng

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

Recent years have witnessed the great success of blind image quality assessment (BIQA) in various task-specific scenarios, which present invariable distortion types and evaluation criteria. However, due to the rigid structure and learning framework, they cannot apply to the cross-task BIQA scenario, where the distortion types and evaluation criteria keep changing in practical applications. This paper proposes a scalable incremental learning framework (SILF) that could sequentially conduct BIQA across multiple evaluation tasks with limited memory capacity. More specifically, we develop a dynamic parameter isolation strategy to sequentially update the task-specific parameter subsets, which are non-overlapped with each other. Each parameter subset is temporarily settled to Remember one evaluation preference toward its corresponding task, and the previously settled parameter subsets can be adaptively reused in the following BIQA to achieve better performance based on the task relevance. To suppress the unrestrained expansion of memory capacity in sequential tasks learning, we develop a scalable memory unit by gradually and selectively pruning unimportant neurons from previously settled parameter subsets, which enable us to Forget part of previous experiences and free the limited memory capacity for adapting to the emerging new tasks. Extensive experiments on eleven IQA datasets demonstrate that our proposed method significantly outperforms the other state-of-the-art methods in cross-task BIQA. The source code of the proposed method is available at https://github.com/maruiperfect/SILF.

Published
2022

3. Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning

Author

Wei, Haoran, Wu, Qingbo, Li, Hui, Ngan, King Ngi, Li, Hongliang, Meng, Fanman, and Xu, Linfeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Due to the lack of natural scene and haze prior information, it is greatly challenging to completely remove the haze from a single image without distorting its visual content. Fortunately, the real-world haze usually presents non-homogeneous distribution, which provides us with many valuable clues in partial well-preserved regions. In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning. Firstly, we employ the gamma correction iteratively to simulate artificial multiple shots under different exposure conditions, whose haze degrees are different and enrich the underlying scene prior. Secondly, beyond utilizing the local neighboring relationship, we build a bidimensional graph reasoning module to conduct non-local filtering in the spatial and channel dimensions of feature maps, which models their long-range dependency and propagates the natural scene prior between the well-preserved nodes and the nodes contaminated by haze. To the best of our knowledge, this is the first exploration to remove non-homogeneous haze via the graph reasoning based framework. We evaluate our method on different benchmark datasets. The results demonstrate that our method achieves superior performance over many state-of-the-art algorithms for both the single image dehazing and hazy image understanding tasks. The source code of the proposed NHRN is available on https://github.com/whrws/NHRNet.

Published
2021
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4. BA^2M: A Batch Aware Attention Module for Image Classification

Author

Cheng, Qishang, Li, Hongliang, Wu, Qingbo, and Ngan, King Ngi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The attention mechanisms have been employed in Convolutional Neural Network (CNN) to enhance the feature representation. However, existing attention mechanisms only concentrate on refining the features inside each sample and neglect the discrimination between different samples. In this paper, we propose a batch aware attention module (BA2M) for feature enrichment from a distinctive perspective. More specifically, we first get the sample-wise attention representation (SAR) by fusing the channel, local spatial and global spatial attention maps within each sample. Then, we feed the SARs of the whole batch to a normalization function to get the weights for each sample. The weights serve to distinguish the features' importance between samples in a training batch with different complexity of content. The BA2M could be embedded into different parts of CNN and optimized with the network in an end-to-end manner. The design of BA2M is lightweight with few extra parameters and calculations. We validate BA2M through extensive experiments on CIFAR-100 and ImageNet-1K for the image recognition task. The results show that BA2M can boost the performance of various network architectures and outperforms many classical attention methods. Besides, BA2M exceeds traditional methods of re-weighting samples based on the loss value., Comment: 11 pages, 5 figures

Published
2021

5. Advanced Geometry Surface Coding for Dynamic Point Cloud Compression

Author

Xiong, Jian, Gao, Hao, Wang, Miaohui, Li, Hongliang, Ngan, King Ngi, and Lin, Weisi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In video-based dynamic point cloud compression (V-PCC), 3D point clouds are projected onto 2D images for compressing with the existing video codecs. However, the existing video codecs are originally designed for natural visual signals, and it fails to account for the characteristics of point clouds. Thus, there are still problems in the compression of geometry information generated from the point clouds. Firstly, the distortion model in the existing rate-distortion optimization (RDO) is not consistent with the geometry quality assessment metrics. Secondly, the prediction methods in video codecs fail to account for the fact that the highest depth values of a far layer is greater than or equal to the corresponding lowest depth values of a near layer. This paper proposes an advanced geometry surface coding (AGSC) method for dynamic point clouds (DPC) compression. The proposed method consists of two modules, including an error projection model-based (EPM-based) RDO and an occupancy map-based (OM-based) merge prediction. Firstly, the EPM model is proposed to describe the relationship between the distortion model in the existing video codec and the geometry quality metric. Secondly, the EPM-based RDO method is presented to project the existing distortion model on the plane normal and is simplified to estimate the average normal vectors of coding units (CUs). Finally, we propose the OM-based merge prediction approach, in which the prediction pixels of merge modes are refined based on the occupancy map. Experiments tested on the standard point clouds show that the proposed method achieves an average 9.84\% bitrate saving for geometry compression.

Published
2021

7. Visual Question Answering Method Based on Counterfactual Thinking

Author

YUAN De-sen, LIU Xiu-jing, WU Qing-bo, LI Hong-liang, MENG Fan-man, NGAN King-ngi, XU Lin-feng

Subjects
visual question answering, causal inference, counterfactual thinking, contrastive learning, deep learning, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

Visual question answering(VQA) is a multi-modal task that combines computer vision and natural language proces-sing,which is extremely challenging.However,the current VQA model is often misled by the apparent correlation in the data,and the output of the model is directly guided by language bias.Many previous researches focus on solving language bias and assisting the model via counterfactual sample methods.These studies,however,ignore the prediction information and the difference between key features and non-key features in counterfactual samples.The proposed model can distinguish the difference between the original sample,the factual sample and the counterfactual sample.In view of this,this paper proposes a paradigm of contrastive learning based on counterfactual samples.By comparing these three samples in terms of feature gaps and prediction gaps,the VQA model has been significantly improved in its robustness.Compared with CL-VQA method,the overall precision,average precision and Num index of this method improves by 0.19%,0.89% and 2.6% respectively.Compared with the CSSVQA method,the Gap of the proposed method decrease to 0.45 from 0.96.

Published
2022
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8. Visibility Constrained Generative Model for Depth-based 3D Facial Pose Tracking

Author

Sheng, Lu, Cai, Jianfei, Cham, Tat-Jen, Pavlovic, Vladimir, and Ngan, King Ngi

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In this paper, we propose a generative framework that unifies depth-based 3D facial pose tracking and face model adaptation on-the-fly, in the unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Specifically, we introduce a statistical 3D morphable model that flexibly describes the distribution of points on the surface of the face model, with an efficient switchable online adaptation that gradually captures the identity of the tracked subject and rapidly constructs a suitable face model when the subject changes. Moreover, unlike prior art that employed ICP-based facial pose estimation, to improve robustness to occlusions, we propose a ray visibility constraint that regularizes the pose based on the face model's visibility with respect to the input point cloud. Ablation studies and experimental results on Biwi and ICT-3DHP datasets demonstrate that the proposed framework is effective and outperforms completing state-of-the-art depth-based methods.

Published
2019

9. MVF-Net: Multi-View 3D Face Morphable Model Regression

Author

Wu, Fanzi, Bao, Linchao, Chen, Yajing, Ling, Yonggen, Song, Yibing, Li, Songnan, Ngan, King Ngi, and Liu, Wei

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We address the problem of recovering the 3D geometry of a human face from a set of facial images in multiple views. While recent studies have shown impressive progress in 3D Morphable Model (3DMM) based facial reconstruction, the settings are mostly restricted to a single view. There is an inherent drawback in the single-view setting: the lack of reliable 3D constraints can cause unresolvable ambiguities. We in this paper explore 3DMM-based shape recovery in a different setting, where a set of multi-view facial images are given as input. A novel approach is proposed to regress 3DMM parameters from multi-view inputs with an end-to-end trainable Convolutional Neural Network (CNN). Multiview geometric constraints are incorporated into the network by establishing dense correspondences between different views leveraging a novel self-supervised view alignment loss. The main ingredient of the view alignment loss is a differentiable dense optical flow estimator that can backpropagate the alignment errors between an input view and a synthetic rendering from another input view, which is projected to the target view through the 3D shape to be inferred. Through minimizing the view alignment loss, better 3D shapes can be recovered such that the synthetic projections from one view to another can better align with the observed image. Extensive experiments demonstrate the superiority of the proposed method over other 3DMM methods., Comment: 2019 Conference on Computer Vision and Pattern Recognition

Published
2019

10. Hierarchy Neighborhood Discriminative Hashing for An Unified View of Single-Label and Multi-Label Image retrieval

Author

Ma, Lei, Li, Hongliang, Wu, Qingbo, Meng, Fanman, and Ngan, King Ngi

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Recently, deep supervised hashing methods have become popular for large-scale image retrieval task. To preserve the semantic similarity notion between examples, they typically utilize the pairwise supervision or the triplet supervised information for hash learning. However, these methods usually ignore the semantic class information which can help the improvement of the semantic discriminative ability of hash codes. In this paper, we propose a novel hierarchy neighborhood discriminative hashing method. Specifically, we construct a bipartite graph to build coarse semantic neighbourhood relationship between the sub-class feature centers and the embeddings features. Moreover, we utilize the pairwise supervised information to construct the fined semantic neighbourhood relationship between embeddings features. Finally, we propose a hierarchy neighborhood discriminative hashing loss to unify the single-label and multilabel image retrieval problem with a one-stream deep neural network architecture. Experimental results on two largescale datasets demonstrate that the proposed method can outperform the state-of-the-art hashing methods.

Published
2019

11. 3D Facial Expression Reconstruction using Cascaded Regression

Author

Wu, Fanzi, Li, Songnan, Zhao, Tianhao, Ngan, King Ngi, and Sheng, Lv

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper proposes a novel model fitting algorithm for 3D facial expression reconstruction from a single image. Face expression reconstruction from a single image is a challenging task in computer vision. Most state-of-the-art methods fit the input image to a 3D Morphable Model (3DMM). These methods need to solve a stochastic problem and cannot deal with expression and pose variations. To solve this problem, we adopt a 3D face expression model and use a combined feature which is robust to scale, rotation and different lighting conditions. The proposed method applies a cascaded regression framework to estimate parameters for the 3DMM. 2D landmarks are detected and used to initialize the 3D shape and mapping matrices. In each iteration, residues between the current 3DMM parameters and the ground truth are estimated and then used to update the 3D shapes. The mapping matrices are also calculated based on the updated shapes and 2D landmarks. HOG features of the local patches and displacements between 3D landmark projections and 2D landmarks are exploited. Compared with existing methods, the proposed method is robust to expression and pose changes and can reconstruct higher fidelity 3D face shape.

Published
2017

21. Cross-Modal Recurrent Semantic Comprehension for Referring Image Segmentation

Author

Shang, Chao, Li, Hongliang, Qiu, Heqian, Wu, Qingbo, Meng, Fanman, Zhao, Taijin, and Ngan, King Ngi

Abstract

Referring image segmentation aims to segment the target object from the image according to the description of language expression. Due to the diversity of language expressions, word sequences in different orders often express different semantic information. The previous methods focus more on matching different words to different visual regions in the image separately, ignoring the global semantic understanding of language expression based on the sequence structure. To address this problem, we redesign a new recurrent network structure for referring image segmentation, called Cross-Modal Recurrent Semantic Comprehension Network (CRSCNet), to obtain a more comprehensive global semantic understanding through iterative cross-modal semantic reasoning. Specifically, in each iteration, we first propose a Dynamic SepConv to extract relevant visual features guided by language and further propose Language Attentional Feature Modulation to improve the feature discriminability, then propose a Cross-Modal Semantic Reasoning module to perform global semantic reasoning by capturing both linguistic and visual information, and finally updates and corrects the visual features of the predicted object based on semantic information. Moreover, we further propose a Cross-Modal ASPP to capture richer visual information referred to in the global semantics of the language expression from larger receptive fields. Extensive experiments demonstrate that our proposed network significantly outperforms previous state-of-the-art methods on multiple datasets.

Published
2023
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22. 2-D order-16 integer transforms for HD video coding

Author

Dong, Jie, Ngan, King Ngi, Fong, Chi-Keung, and Cham, Wai-Kuen

Subjects
Algorithms -- Research, Image coding -- Methods, High definition video -- Methods, Transformations (Mathematics) -- Usage, Algorithm, Business, Computers, Electronics, Electronics and electrical industries
Published
2009

25. 3-D shape-adaptive directional wavelet transform for object-based scalable video coding

Author

Liu, Yu, Ngan, King Ngi, and Wu, Feng

Subjects
Image coding -- Technology application, Wavelet transforms -- Evaluation, Adaptive control -- Methods, Technology application, Business, Computers, Electronics, Electronics and electrical industries
Abstract

This paper presents a 3-D shape-adaptive directional wavelet coding technique for object-based scalable video coding. This technique includes 3-D object-based directional threading and extensions of weighted adaptive lifting (WAL) scheme. The 3-D object-based directional threading, which unifies the concept of temporal motion threading and 2-D spatial directional threading, provides the opportunities to align a series of video object planes to form a 3-D video object and exploit the spatio-temporal correlation inside the 3-D video object. The WAL scheme, which is extended from 2-D frame-based image coding to 3-D object-based video coding, is employed to decompose the 3-D video object into a 3-D multispatio-temporal resolution video object pyramid for object-based scalable video coding. Experimental results show that the proposed 3-D shape-adaptive directional wavelet coding technique consistently outperforms MPEG-4 and other wavelet-based schemes for coding arbitrarily shaped video objects. Index Terms--Arbitrarily shaped video object, directional wavelet transform, object-based coding, scalable video coding, weighted adaptive lifting (WAL), 3-D directional threading.

Published
2008

26. Improving WLAN VoIP capacity through service differentiation

Author

Gao, Deyun, Cai, Jianfei, Foh, Chuan Heng, Lau, Chiew-Tong, and Ngan, King Ngi

Subjects
VoIP (Network protocol) -- Evaluation, Telephone services -- Service enhancement, Wireless local area networks (Computer networks) -- Design and construction, Voice over IP, Wireless LAN/WAN system, Wireless network, Business, Electronics, Electronics and electrical industries, Transportation industry
Abstract

Voice over Internet protocol (VoIP) is one of the most important applications for IEEE 802.11 wireless local area networks (WLANs). For network planners who deploy VoIP over WLANs, one of the important issues is the VoIP capacity. Directly implementing VoIP over infrastructure WLANs will create the bottleneck problem at the access point (AP). In this paper, we propose the use of the service differentiation provided by the new IEEE 802.11e standard to solve the bottleneck problem and improve voice capacity. In particular, we propose the allocation of a higher priority access category (AC) to the AP while allocating lower priority AC to mobile stations. We develop a simple Markov chain model, which considers the important enhanced distributed channel access (EDCA) parameters and the channel errors under saturation and nonsaturation conditions. Based on the developed analytical model, we analyze the performance of VoIP over EDCA. By appropriately selecting the EDCA parameters, we are able to differentiate the services for the downlink and uplink. The experimental results are very promising. With the adjustment of only one EDCA parameter, we improve the VoIP capacity by 20%-30%. Index Terms--Enhanced distributed channel access (EDCA), IEEE 802.11e wireless local area networks (WLANs), medium access mechanisms, service differentiation, voice capacity, voice over Internet protocol (VoIP).

Published
2008

27. Optimized cross-layer design for scalable video transmission over the IEEE 802.11e networks

Author

Foh, Chuan Heng, Zhang, Yu, Ni, Zefeng, Cai, Jianfei, and Ngan, King Ngi

Subjects
Wireless local area networks (Computer networks) -- Research, Wi-Fi -- Research, Image processing -- Methods, Image transmission -- Usage, Wireless LAN/WAN system, Wireless network, Business, Computers, Electronics, Electronics and electrical industries
Abstract

A cross-layer design for optimizing 3-D wavelet scalable video transmission over the IEEE 802.11e networks is proposed. A thorough study on the behavior of the IEEE 802.11e protocol is conducted. Based on our findings, all timescales rate control is developed featuring a unique property of soft capacity support for multimedia delivery. The design consists of a macro timescale and a micro timescale rate control schemes residing at the application layer and the network sublayer respectively. The macro rate control uses bandwidth estimation to achieve optimal bit allocation with minimum distortion. The micro rate control employs an adaptive mapping of packets from video classifications to appropriate network priorities which preemptively drops less important video packets to maximize the transmission protection to the important video packets. The performance is investigated by simulations highlighting advantages of our cross-layer design. Index Terms--Multimedia transmission, network performance analysis, scalable video, wireless LAN.

Published
2007

28. A unified approach of bit-rate control for binary and gray-level shape sequences coding

Author

Chen, Zhenzhong and Ngan, King Ngi

Subjects
Image coding -- Equipment and supplies, Electronics -- Research, Business, Computers, Electronics, Electronics and electrical industries
Abstract

This paper describes a unified framework of bit-rate control for binary and gray-level shape coding. An exponential rate-distortion model is proposed for binary shape and used in the bit-rate control. An approximation method is applied for finer distortion scale adjustment by adjusting the conversion ratio at the binary alpha block level. Since the gray-level shape is encoded as its support information and the alpha plane information, the rate-quantizer relationship of the alpha plane is modeled. The bit-rate control is then able to adjust the quantizer of the alpha plane for the bit-rate control purpose. The extension for video object (VO) coding is also studied. In this bit-rate control approach, a buffer feedback controller is employed to determine the target bit rates for different sources, i.e., binary shape, gray-level shape, and texture. Accordingly, the distortion scales of different sources are adjusted based on their rate-distortion relationships to maintain a stable encoder buffer. The advantages of the proposed method are demonstrated through various tests for different scenarios. Overall, the algorithm can efficiently encode the shape information and shows a good performance for VO coding. Index Terms--Binary shape, gray-level shape, MPEG-4, rate control, rate-distortion, shape coding.

Published
2007

29. 3-D object-based scalable wavelet video coding with boundary effect suppression

Author

Liu, Yu, Wu, Feng, and Ngan, King Ngi

Subjects
Wavelet transforms -- Analysis, Image coding -- Analysis, Business, Computers, Electronics, Electronics and electrical industries
Abstract

This paper extends the lifting-based motion threading (MTh) technique from the frame-based coding to the object-based coding, attracted by the unique advantages of the object-based coding that do not exist in other coding schemes. The boundary effects, which exist in spatial and temporal wavelet transforms due to the manner of object-based coding, are well suppressed by 3-D shape-adaptive discrete wavelet transform (3D SA-DWT) via the lifting implementation in a unified framework. Based on the proposed object-based MTh technique, a 3-D object-based scalable wavelet video coder is developed. Experimental results show that the proposed coder achieves good performance compared with the existing object-based coders and provides the scalability functionality at the same time. Index Terms--Boundary effects, object-based motion threading, wavelet video object coding, 3-D shape-adaptive discrete wavelet transform (3D SA-DWT).

Published
2007

30. Towards rate-distortion tradeoff in real-time color video coding

Author

Chen, Zhenzhong and Ngan, King Ngi

Subjects
Image coding -- Research, Electric distortion -- Research, Electric distortion -- Control, Buffering (Computers) -- Research, Business, Computers, Electronics, Electronics and electrical industries
Abstract

In this paper, we address the key problem in real-time video coding, the rate-distortion (R-D) tradeoff. As most video coding applications employ color images, we analyze the R-D modeling problem of the color image sequence. We investigate that the R-D characteristics of color video signal in traditional transform-based video coding systems should be modeled for the luminance and chrominance components separately. So we propose separable R-D models for color video coding. The feedback from the encoder buffer is analyzed by a control-theoretic adaptation approach to avoid buffer overflow and underflow. To achieve smooth video quality and satisfy the delay constraints in real-time applications, a novel R-D tradeoff controller is designed. In the proposed R-D tradeoff framework, both the quality variation and buffer safety are considered. Extensive simulation results demonstrate that the proposed approach can achieve smooth video quality without sacrificing overall quality while efficiently utilize the bandwidth without buffer overflow or underflow. Index Terms--Buffer control, H.264, rate control, rate-distortion (R-D) model, R-D tradeoff, video coding.

Published
2007

31. 4-D wavelet-based multiview video coding

Author

Yang, Wenxian, Lu, Yan, Wu, Feng, Cai, Jianfei, Ngan, King Ngi, and Li, Shipeng

Subjects
Image coding -- Research, Wavelet analysis, Business, Computers, Electronics, Electronics and electrical industries
Abstract

The conventional multiview video coding (MVC) schemes, utilizing both neighboring temporal frames and view frames as possible references, have only shown a slight gain over those using temporal frames alone in terms of coding efficiency. The reason for this is that the neighboring temporal frames exhibit stronger correlation with the current frame and the view frames often fail to be selected as references. This paper proposes an elegant MVC framework using high dimensional wavelet, which rightly matches the inherent high dimension property of multiview video. It also makes a better usage of both temporal and view correlations thanks to the hierarchical decomposition. Besides the proposed framework, this paper also investigates MVC coding from the following aspects. First, a disparity-compensated view filter (DCVF) with pixel alignment is proposed, which can accommodate both global and local view disparities among view frames. The proposed DCVF and the existing motion-compensated temporal filter (MCTF) unify the view and temporal decompositions as a generic lifting transform. Second, an adaptive decomposition structure based on the analysis of the temporal and view correlations is proposed. A Lagrangian cost function is derived to determine the optimum decomposition structure. Third, the major components of the proposed MVC coding are figured out, including macroblock type design, subband coefficient coding, and rate allocation. Extensive experiments are carried out on the MPEG 3DAV test sequences and the superior performance of the proposed MVC coding is demonstrated. In addition, the proposed MVC framework can easily support temporal, spatial, SNR, as well as view scalabilities. Index Terms--Global and local motion alignment, high dimensional wavelet, lifting, multiview video coding (MVC).

Published
2006

32. Dynamic programming-based reverse frame selection for VBR video delivery under constrained resources

Author

Tao, Dayong, Cai, Jianfei, Yi, Haoran, Rajan, Deepu, Chia, Liang-Tien, and Ngan, King Ngi

Subjects
Bandwidth -- Research, Dynamic programming -- Usage, Bandwidth allocation, Bandwidth technology, Business, Computers, Electronics, Electronics and electrical industries
Abstract

In this paper, we investigate optimal frame-selection algorithms based on dynamic programming for delivering stored variable bit rate (VBR) video under both bandwidth and buffer size constraints. Our objective is to find a feasible set of frames that can maximize the video's accumulated motion values without violating any constraint. It is well known that dynamic programming has high complexity. In this research, we propose to eliminate nonoptimal intermediate frame states, which can effectively reduce the complexity of dynamic programming. Moreover, we propose a reverse frame selection (RFS) algorithm, where the selection starts from the last frame and ends at the first frame. Compared with the conventional dynamic programming-based forward frame selection, the RFS is able to find all of the optimal results for different preloads in one round. We further extend the RFS scheme to solve the problem of frame selection for VBR channels. In particular, we first perform the RFS algorithm offline, and the complexity is modest and scalable with the aids of frame stuffing and nonoptimal state elimination. During online streaming, we only need to retrieve the optimal frame-selection path from the pregenerated offline results, and it can be applied to any VBR channels as long as the VBR channels can be modeled as piecewise CBR channels. Experimental results show good performance of our proposed algorithms. Index Terms--Bandwidth smoothing, dynamic programming, motion awareness, optimal frame selection, variable bit rate (VBR) channels, VBR video delivery.

Published
2006

33. Joint texture-shape optimization for MPEG-4 multiple video objects

Author

Chen, Zhenzhong and Ngan, King Ngi

Subjects
Image coding -- Research, Algorithms -- Research, Algorithms -- Technology application, Algorithm, Technology application, Business, Computers, Electronics, Electronics and electrical industries
Abstract

In this paper, we present a uniform framework for optimal multiple video object bit allocation in MPEG-4. We combine the rate-distortion (R-D) models for the texture and shape information of arbitrarily shaped video objects to develop the joint texture-shape R-D models. The dynamic programming technique is applied to optimize the bit allocation for the multiple video objects. The simulation results demonstrate that the proposed joint texture-shape optimization algorithm outperforms the MPEG-4 verification model on the decoded picture quality. Index Terms--Bit ailocation, joint texture-shape optimization, MPEG-4, multiple video object, rate control, rate-distortion (R-D).

Published
2005

34. Admission control in IEEE 802.11e wireless LANs

Author

Gao, Deyun, Cai, Jianfei, and Ngan, King Ngi

Subjects
Protocol, Multimedia technology, Wireless network, Wireless LAN/WAN system, Network access, Market trend/market analysis, Company business management, Computer network protocols -- Standards, Computer network protocols -- Forecasts and trends, Multimedia technology -- Management, Wireless local area networks (Computer networks) -- Access control, Wireless local area networks (Computer networks) -- Standards, Access control (Computers) -- Methods
Published
2005

37. Linear rate-distortion models for MPEG-4 shape coding

Author

Chen, Zhenzhong and Ngan, King Ngi

Subjects
Business, Computers, Electronics, Electronics and electrical industries
Abstract

In this letter, we explore the rate-distortion (R-D) characteristics of binary shape information in the MPEG-4 standard. The shape coding scheme is a block-based context-based arithmetic encoding approach and distortion is introduced by down and up sampling. Generally, the shape bit rate and nonnormalized distortion increase in proportion with the number of border blocks. At any given resolution scale, the more complex an object, the more distortion that is introduced. Consequently, we propose an R-D model based on the parameters derived from the border blocks and a block-based shape complexity for the video object. The computational complexity is much lower than other existing methods. Experimental results show that it can accurately predict the bit rate and distortion of the binary shape for rate control purposes. Index Terms--MPEG-4, rate distortion (R-D), shape coding.

Published
2004

42. An efficient algorithm for H.264/AVC high definition video coding

Author

Chen, Zhenzhong, Zhang, Dongdong, and Ngan, King Ngi

Subjects
Video recordings -- Analysis, High definition video -- Methods, Image coding -- Analysis, Business, Electronics and electrical industries, Engineering and manufacturing industries
Abstract

Quality consistency has been an important issue in video applications. However, the quality fluctuation cannot be avoided in single pass video coding due to the variation of the video content, flexible coding structure, different frame types, etc. In this paper, we propose an efficient method to reduce the quality fluctuation for H.264/AVC high definition (HD) video coding. We aim to reduce the distortion variation among frames at different types. A single-pass distributed control algorithm is introduced which provides different control mechanism for different frame type. For P frame, the quantization parameter is decided by the relationship between the quantization step and the given target bit rate and the buffer status to satisfy the bit rate constraints. For B frame, the quantization parameter is determined by the relationship between the quantization step and the given target distortion to reduce the quality fluctuation between P and B frames. Extensive simulation results are provided to demonstrate the efficiency of the proposed method. When compared to the traditional method used in current H.264/AVC video coding, the quality fluctuations are significantly reduced. Moreover, the bit rate constraints of the high definition video coding are satisfied. Index Terms--H.264/AVC, rate-distortion, rate control, high definition, video coding.

Published
2008

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