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2021 IEEE International Conference on multimedia and Expo, ICME 2021

作者： Hu, Chenhao Hu, Ruimin Wang, Xiaochen Wu, Yulin Liu, Wenke National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Research Institute of Wuhan University Shenzhen China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

ISBN: (纸本)9781665438643

Spatial audio object coding (SAOC) is an effective method to transmit multiple audio objects. Audio systems can provide personalized services under this framework. However, this method causes frequency aliasing distortion, which severely impacts the listening experience. The multi-step SAOC (MS-SAOC) scheme was proposed to enhance the sound quality of each audio object by using residual information. Compared with SAOC, the bit-rate increases three times due to the residual data of multiple objects. In this paper, an efficient multi-step residual coding method is proposed to reduce the residual bit-rate of MS-SAOC. A two-level filter is designed to remove redundant residual information, and the limited residual information can efficiently compensate for frequency aliasing distortion. From experiment results, the residual bit-rate is half of MS-SAOC, and the sound quality is maintained at the Good-Excellent level. © 2021 IEEE

关键词： Audio systems

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SPATIAL AUDIO OBJECT CODING BASED ON TIME-FREQUENCY SHIFTING AND SCHEDULING

SPATIAL AUDIO OBJECT CODING BASED ON TIME-FREQUENCY SHIFTING...

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2021 IEEE International Conference on multimedia and Expo, ICME 2021

作者： Hu, Chenhao Hu, Ruimin Wang, Xiaochen Wu, Yulin National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Research Institute of Wuhan University Shenzhen China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

ISBN: (纸本)9781665438643

Spatial audio object coding (SAOC) is an effective method to transmit multiple audio objects. It divides the full frequency band into 28 subbands and extracts spatial parameters for decoding. In this way, objects can be encoded into a downmix signal with a few parameters. However, using the same parameters in one subband will cause frequency aliasing distortion, which severely impacts the listening experience. Existing studies to enhance SAOC cannot eliminate the aliasing distortion of all objects effectively. This paper describes a new structure to balance the bit-rate and decode quality based on time-frequency (TF) shifting and scheduling. In this structure, a TF shifting strategy (contains global shifting and local shifting) is proposed to reduce frequency aliasing distortion. Furthermore, a scheduling strategy is used to decide which part should be shifted according to the degree of aliasing. From the experiment results, the performance of the proposed method is better than SAOC and other enhanced methods. © 2021 IEEE Computer Society. All rights reserved.

关键词： Decoding

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LOW BITRATES AUDIO OBJECT CODING USING CONVOLUTIONAL AUTO-ENCODER AND DENSENET MIXTURE MODEL

LOW BITRATES AUDIO OBJECT CODING USING CONVOLUTIONAL AUTO-EN...

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2021 IEEE International Conference on multimedia and Expo, ICME 2021

作者： Wu, Yulin Hu, Ruimin Hu, Chenhao Ke, Shanfa Li, Gang Wang, Xiaochen National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Research Institute of Wuhan University Shenzhen China

ISBN: (纸本)9781665438643

The efficient transmission of the audio objects can be achieved by spatial audio object coding (SAOC) method that conveys a mono downmix signal together with side information parameters that enable object reconstruction in the decoder. To allow the transmission of audio objects at low bitrates, we present a new audio coding method with convolutional auto-encoder (CAE) and dense convolutional network (DenseNet) mixture model, optimizing the compression of side information parameters of audio objects. It has two main advantages: 1) Different from the linear transform methods, CAE can dig the nonlinear relationship of side information parameters and can effectively reduce the dimension of side information parameters;2) DenseNet is adding in the decoder to make full use of low dimensional features of side information parameters, which improves the audio quality at low bitrate. Experiments show that our method outperforms baseline methods permitting bitrates as low as 1 kbps per object. © 2021 IEEE

关键词： Mixtures

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Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL

Learning from Imperfect Data: Towards Efficient Knowledge Di...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhong, Qihuang Chen, Kunfeng Ding, Liang Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The University of Sydney Australia Nanyang Technological University Singapore

ISBN: (纸本)9798891761681

Large Language Models (LLMs) have shown promising performance in text-to-SQL, which involves translating natural language questions into SQL queries. However, current text-to-SQL LLMs are computationally expensive and challenging to deploy in real-world applications, highlighting the importance of compressing them. To achieve this goal, knowledge distillation (KD) is a common approach, which aims to distill the larger teacher model into a smaller student model. While numerous KD methods for autoregressive LLMs have emerged recently, it is still under-explored whether they work well in complex text-to-SQL scenarios. To this end, we conduct a series of analyses and reveal that these KD methods generally fall short in balancing performance and efficiency. In response to this problem, we propose to improve the KD with Imperfect Data, namely KID, which effectively boosts the performance without introducing much training budget. The core of KID is to efficiently mitigate the training-inference mismatch by simulating the cascading effect1 of inference in the imperfect training data. Extensive experiments on 5 text-to-SQL benchmarks show that, KID can not only achieve consistent and significant performance gains (up to +5.83% average score) across all model types and sizes, but also effectively improve the training efficiency. © 2024 Association for Computational Linguistics.

关键词： Budget control

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Unequal Error Protection Based on Expanding Window Fountain for Object-Based 3D Audio

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Wuhan University Journal of Natural Sciences 2017年第4期22卷 323-328页

作者： YANG Cheng HU Ruimin SONG Yucheng SU Liuyue WANG Xiaochen CHEN Wei National Engineering Research Center for Multimedia Software/School of Computer Science Wuhan UniversityWuhan 430072HubeiChina Hubei Key Laboratory of Multimedia and Network Communication Engineering(Wuhan University) Wuhan 430072HubeiChina Research Institute of Wuhan University in Shenzhen Shenzhen 518000GuangdongChina School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001GuizhouChina

This paper proposes an unequal error protection（UEP）coding method to improve the transmission performance of three-dimensional（3D）audio based on expanding window fountain（EWF）.Different from other transmissions with equal error protection（EEP）when transmitting the 3D audio *** approach of extracting the important audio object is presented,and more protection is given to more important audio object and comparatively less protection is given to the normal audio *** and subjective experiments have shown that the proposed UEP method achieves better performance than equal error protection method,while the bits error rates（BER）of the important audio object can decrease from 10^（–3） to 10^（–4）,and the subjective quality of UEP is better than that of EEP by 14%.

关键词： object-based 3D audio unequal error protection equal error protection

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Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification 33

Ranking Aggregation with Interactive Feedback for Collaborat...

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Huang, Ji Liang, Chao Zhang, Yue Wang, Zhongyuan Zhang, Chunjie National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China

Person re-identification (re-ID) aims to retrieve the same person from a group of networking cameras. Ranking aggregation (RA), a method to aggregates multiple ranking results, can further improve the retrieval accuracy in re-ID tasks. Existing RA work can be generally divided into unsupervised methods and fully-supervised methods. Unsupervised methods lack external supervision, can hardly achieve the optimal results. In contrast, fully-supervised methods need massive labeling data for training, which is prohibitively expensive in the practical application. This paper studies interactive RA (IRA) to address the above challenges in existing RA research. The core idea is to utilize a small amount of supervisory information, obtained from users' relevance feedback, to supervise RA method to produce better re-ranking results. Compared with unsupervised methods, IRA introduces supervisory information and thus has better aggregation accuracy. Compared with fully-supervision methods, the supervisory information of IRA is more readily available, and can be targeted to specific queries. Particularly, we propose two IRA implementations, based on ranking positions and scores respectively, to adapt to diverse application scenarios, where rankers only give rankings, or rankers give similarity scores. Experiments on three public re-ID datasets have shown that IRA significantly outperforms the-state-of-art unsupervised baselines, and achieves similar accuracy with less labeling cost than the fully-supervised RA method. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Computer vision

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AUTOMATICALLY ADJUSTABLE MULTI-SCALE FEATURE EXTRACTION FRAMEWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION

AUTOMATICALLY ADJUSTABLE MULTI-SCALE FEATURE EXTRACTION FRAM...

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2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Yang, Jiaqi Du, Bo Wu, Chen Zhang, Liangpei State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China

Recently, deep learning-based methods have shown the great potential in hyperspectral image (HSI) classification. Nevertheless, feature extraction by convolutional neural network (CNN) is often performed on only one scale, resulting in multi-scale information loss. To address this problem, in this paper, we propose an automatically adjustable multi-scale feature extraction framework (A2MFE-Framework) for hyperspectral classification, including a scale reference network and two scale transformation networks. With the well-designed architecture, A2MFE-Framework can not only extract multi-scale features, but also automatically change the network structure to match input features of different scales. Experimental results on two benchmark HSI datasets demonstrate that the A2MFE-Framework can better capture multi-scale features of different objects via an automatically adjustable feature extraction framework with higher classification accuracy compared with previous methods. ©2021 IEEE

关键词： Feature extraction

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A sensitive object-oriented approach to big surveillance data compression for social security applications in smart cities

A sensitive object-oriented approach to big surveillance dat...

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作者： Xiao, Jing Wang, Zhongyuan Chen, Yu Liao, Liang Xiao, Jun Zhan, Gen Hu, Ruimin State Key Laboratory of Software Engineering Computer School of Wuhan University China National Engineering Research Center for Multimedia Software Computer School of Wuhan University China Hubei Provincial Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Collaborative Innovation Center of Geospatial Technology China

Surveillance has become a fairly common practice with the global boom in "smart cities". How to efficiently store and manage the vast quantities of surveillance data is a persistent challenge in terms of analyzing social security problems. Developing data compression technology under the analytic requirements of surveillance data is the key to solving the storage problem. Criminal investigation demands the quality preservation of sensitive objects, typically pedestrians, human faces, vehicles, and license plates;however, the analytical value of surveillance data is rapidly lost as the compression ratio increases. In this paper, we propose a sensitive object-oriented regions of interest-based coding strategy for preserving the analytical value of surveillance data. In the proposed method, instead of generating a saliency map based on human visual perception, we consider saliency as a set of characteristics important for object detection and recognition. By making this modification, almost all sensitive objects necessary in a criminal investigation are assigned high saliency value rather than only one or two salient regions. Motions in the temporal domain are integrated to place emphasis on moving objects, namely moving sensitive objects, which then gain the highest saliency. Finally, a saliency-based rate control algorithm embedded in High Efficiency Video Coding is used to maintain the quality of sensitive objects in the encoded video under a fixed bitrate. Experiments were conducted on two analytical indexes: Feature similarity and object detection accuracy. The results showed that by achieving the same feature similarity and object detection accuracy, our method can save 20% and 40% bitrate over High Efficiency Video Coding, respectively, for the storage of big surveillance data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Object detection

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A format-compliant secure approach for video encryption

A format-compliant secure approach for video encryption

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25th PCS: Picture Coding Symposium 2006, PCS2006

作者： Yang, Zhiyun Xu, Zhengquan Li, Wei Chen, Lin Center of Multimedia and Network Communication Engineering State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China Computer Science College Yangtze University Wuhan 434012 China Center of Multimedia and Network Communication Engineering State Key Laboratory for Information Engineering in Surveying Mapping and Remote Sensing Wuhan University 129 Luoyu Road Wuhan HuBei 430079 China

ISBN: (纸本)300018726X

VLC(Variable length coded) codewords constitute the main part of video data, and the problem of VLC codewords encryption is a very difficult issue. This paper proposed a novel method called VLCEA (VLC codeword Encryption Algorithm) to encrypt VLC codewords in video bitstream. Compared with directly encrypting video data using stream cipher, the algorithm has the same strong anti-attack ability while keeping compression rate and format-compliance, and its speed is about thirteen times faster than stream cipher method. The experimental results demonstrate that the encrypted video is fully disguised. The method is applicable to MPEG-x, H.26x, and JPEG signal.

关键词： Cryptography

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Stacked Sparse Autoencoder for Audio Object Coding 1

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27th International Conference on multimedia Modeling, MMM 2021

作者： Wu, Yulin Hu, Ruimin Wang, Xiaochen Hu, Chenhao Li, Gang National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Collaborative Innovation Center of Geospatial Technology Wuhan China

ISBN: (数字)9783030678326

ISBN: (纸本)9783030678319

Compared with channel-based audio coding, the object-based audio coding has a definite advantage in meeting the user’s demands of personalized control. However, in the conventional Spatial Audio Object Coding (SAOC), each frame is divided into 28 sub-bands. All frequency points in one sub-band share the common parameter. Under the SAOC framework, the bitrate can be saved, but aliasing distortion is prone to occur, which will influence the listening experience of audiences. In order to obtain higher perceptual quality, we propose a Stacked Sparse Autoencoder (SSAE) pipeline as overlapped modules. Each module extracted the efficient feature of side information from its preceding module. Then we can reduce the dimensionality of side information parameters for saving bitrate, and well reconstruct audio objects, thereby providing favorable auditory perception. Compared with conventional SAOC, TS-SAOC, and SVD-SAOC, both objective and subjective results show that the proposed method can achieve the best sound quality of the output signal at the same bitrate. © 2021, Springer Nature Switzerland AG.

关键词： Learning systems

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