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Guan-Ming Su

Bio: Guan-Ming Su is an academic researcher from Dolby Laboratories. The author has contributed to research in topics: Pixel & High dynamic range. The author has an hindex of 21, co-authored 120 publications receiving 1469 citations. Previous affiliations of Guan-Ming Su include University of Maryland, College Park & Guilford Technical Community College.


Papers
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Proceedings ArticleDOI
29 Oct 2007
TL;DR: The proposed methods form the first steps to bring together advanced information retrieval and secure search capabilities for a wide range of applications including managing data in government and business operations, enabling scholarly study of sensitive data, and facilitating the document discovery process in litigation.
Abstract: This paper introduces a new framework for confidentiality preserving rank-ordered search and retrieval over large document collections. The proposed framework not only protects document/query confidentiality against an outside intruder, but also prevents an untrusted data center from learning information about the query and the document collection. We present practical techniques for proper integration of relevance scoring methods and cryptographic techniques, such as order preserving encryption, to protect data collections and indices and provide efficient and accurate search capabilities to securely rank-order documents in response to a query. Experimental results on the W3C collection show that these techniques have comparable performance to conventional search systems designed for non-encrypted data in terms of search accuracy. The proposed methods thus form the first steps to bring together advanced information retrieval and secure search capabilities for a wide range of applications including managing data in government and business operations, enabling scholarly study of sensitive data, and facilitating the document discovery process in litigation.

171 citations

Patent
21 Dec 2005
TL;DR: In this article, a rate-adaptation unit may be configured to receive local as well as end-to-end feedback information associated with data transmission (such as data delay, packet loss, transmit power headroom, channel condition, sector loading, the amount of buffered data, etc.) from a wireless access module in communication with wireless/wired networks.
Abstract: Embodiments described herein relate to providing adaptive encoding of real-time information in packet-switched wireless communication systems. In an embodiment, a rate-adaptation unit may be configured to receive local as well as end-to-end feedback information associated with data transmission (such as data delay, packet loss, transmit power headroom, channel condition, sector loading, the amount of buffered data, etc.) from a wireless access module in communication with wireless/wired networks, and adapt the real-time information encoding in accordance with such feedback information.

62 citations

Patent
Guan-Ming Su1, Sheng Qu1, Hubert Koepfer1, Yufei Yuan1, Samir N. Hulyalkar1 
13 Apr 2012
TL;DR: In this paper, multi-channel multiple regression (MMR) models are applied to the efficient coding of images and video signals of high dynamic range, and closed form solutions for the prediction parameters are presented for a variety of MMR models.
Abstract: Inter-color image prediction is based on multi-channel multiple regression (MMR) models. Image prediction is applied to the efficient coding of images and video signals of high dynamic range. MMR models may include first order parameters, second order parameters, and cross-pixel parameters. MMR models using extension parameters incorporating neighbor pixel relations are also presented. Using minimum means-square error criteria, closed form solutions for the prediction parameters are presented for a variety of MMR models.

57 citations

Patent
01 Nov 2012
TL;DR: In this paper, a base layer and one or more enhancement layers may be used to carry video signals, wherein the base layer cannot be decoded and viewed on its own, and the image data in the enhancement layer video signals may comprise residual values, quantization parameters, and mapping parameters based in part on a prediction method corresponding to a specific method used in the advanced quantization.
Abstract: Techniques use multiple lower bit depth codecs to provide higher bit depth, high dynamic range, images from an upstream device to a downstream device. A base layer and one or more enhancement layers may be used to carry video signals, wherein the base layer cannot be decoded and viewed on its own. Lower bit depth input image data to base layer processing may be generated from higher bit depth high dynamic range input image data via advanced quantization to minimize the volume of image data to be carried by enhancement layer video signals. The image data in the enhancement layer video signals may comprise residual values, quantization parameters, and mapping parameters based in part on a prediction method corresponding to a specific method used in the advanced quantization. Adaptive dynamic range adaptation techniques take into consideration special transition effects, such as fade-in and fade-outs, for improved coding performance.

53 citations

Journal ArticleDOI
TL;DR: The proposed framework to transmit multiple scalable video programs over downlink multiuser orthogonal frequency division multiplex (OFDM) networks in real time can achieve a desired tradeoff between fairness and efficiency.
Abstract: In this paper, we propose a framework to transmit multiple scalable video programs over downlink multiuser orthogonal frequency division multiplex (OFDM) networks in real time. The framework explores the scalability of the video codec and multidimensional diversity of multiuser OFDM systems to achieve the optimal service objectives subject to constraints on delay and limited system resources. We consider two essential service objectives, namely, the fairness and efficiency. Fairness concerns the video quality deviation among users who subscribe the same quality of service, and efficiency relates to how to attain the highest overall video quality using the available system resources. We formulate the fairness problem as minimizing the maximal end-to-end distortion received among all users and the efficiency problem as minimizing total end-to-end distortion of all users. Fast suboptimal algorithms are proposed to solve the above two optimization problems. The simulation results demonstrated that the proposed fairness algorithm outperforms a time division multiple (TDM) algorithm by 0.5 ~ 3 dB in terms of the worst received video quality among all users. In addition, the proposed framework can achieve a desired tradeoff between fairness and efficiency. For achieving the same average video quality among all users, the proposed framework can provide fairer video quality with 1 ~ 1.8 dB lower PSNR deviation than a TDM algorithm

53 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper proposes a basic idea for the MRSE based on secure inner product computation, and gives two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models and further extends these two schemes to support more search semantics.
Abstract: With the advent of cloud computing, data owners are motivated to outsource their complex data management systems from local sites to the commercial public cloud for great flexibility and economic savings. But for protecting data privacy, sensitive data have to be encrypted before outsourcing, which obsoletes traditional data utilization based on plaintext keyword search. Thus, enabling an encrypted cloud data search service is of paramount importance. Considering the large number of data users and documents in the cloud, it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to these keywords. Related works on searchable encryption focus on single keyword search or Boolean keyword search, and rarely sort the search results. In this paper, for the first time, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted data in cloud computing (MRSE). We establish a set of strict privacy requirements for such a secure cloud data utilization system. Among various multi-keyword semantics, we choose the efficient similarity measure of "coordinate matching," i.e., as many matches as possible, to capture the relevance of data documents to the search query. We further use "inner product similarity" to quantitatively evaluate such similarity measure. We first propose a basic idea for the MRSE based on secure inner product computation, and then give two significantly improved MRSE schemes to achieve various stringent privacy requirements in two different threat models. To improve search experience of the data search service, we further extend these two schemes to support more search semantics. Thorough analysis investigating privacy and efficiency guarantees of proposed schemes is given. Experiments on the real-world data set further show proposed schemes indeed introduce low overhead on computation and communication.

979 citations

Journal ArticleDOI
TL;DR: This paper constructs a special tree-based index structure and proposes a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents.
Abstract: Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF $\;\times\;$ IDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.

976 citations

01 Jan 2003
TL;DR: A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers.
Abstract: A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

916 citations

Proceedings ArticleDOI
Cong Wang1, Ning Cao, Jin Li1, Kui Ren1, Wenjing Lou 
21 Jun 2010
TL;DR: This paper defines and solves the problem of effective yet secure ranked keyword search over encrypted cloud data, and proposes a definition for ranked searchable symmetric encryption, and gives an efficient design by properly utilizing the existing cryptographic primitive, order-preserving asymmetric encryption (OPSE).
Abstract: As Cloud Computing becomes prevalent, sensitive information are being increasingly centralized into the cloud. For the protection of data privacy, sensitive data has to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow users to securely search over encrypted data through keywords, these techniques support only boolean search, without capturing any relevance of data files. This approach suffers from two main drawbacks when directly applied in the context of Cloud Computing. On the one hand, users, who do not necessarily have pre-knowledge of the encrypted cloud data, have to post process every retrieved file in order to find ones most matching their interest, On the other hand, invariably retrieving all files containing the queried keyword further incurs unnecessary network traffic, which is absolutely undesirable in today's pay-as-you-use cloud paradigm. In this paper, for the first time we define and solve the problem of effective yet secure ranked keyword search over encrypted cloud data. Ranked search greatly enhances system usability by returning the matching files in a ranked order regarding to certain relevance criteria (e.g., keyword frequency), thus making one step closer towards practical deployment of privacy-preserving data hosting services in Cloud Computing. We first give a straightforward yet ideal construction of ranked keyword search under the state-of-the-art searchable symmetric encryption (SSE) security definition, and demonstrate its inefficiency. To achieve more practical performance, we then propose a definition for ranked searchable symmetric encryption, and give an efficient design by properly utilizing the existing cryptographic primitive, order-preserving symmetric encryption (OPSE). Thorough analysis shows that our proposed solution enjoys ``as-strong-as-possible" security guarantee compared to previous SSE schemes, while correctly realizing the goal of ranked keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.

768 citations

Journal ArticleDOI
TL;DR: A novel distance metric, Finger-Earth Mover's Distance (FEMD), is proposed, which only matches the finger parts while not the whole hand, it can better distinguish the hand gestures of slight differences.
Abstract: The recently developed depth sensors, e.g., the Kinect sensor, have provided new opportunities for human-computer interaction (HCI). Although great progress has been made by leveraging the Kinect sensor, e.g., in human body tracking, face recognition and human action recognition, robust hand gesture recognition remains an open problem. Compared to the entire human body, the hand is a smaller object with more complex articulations and more easily affected by segmentation errors. It is thus a very challenging problem to recognize hand gestures. This paper focuses on building a robust part-based hand gesture recognition system using Kinect sensor. To handle the noisy hand shapes obtained from the Kinect sensor, we propose a novel distance metric, Finger-Earth Mover's Distance (FEMD), to measure the dissimilarity between hand shapes. As it only matches the finger parts while not the whole hand, it can better distinguish the hand gestures of slight differences. The extensive experiments demonstrate that our hand gesture recognition system is accurate (a 93.2% mean accuracy on a challenging 10-gesture dataset), efficient (average 0.0750 s per frame), robust to hand articulations, distortions and orientation or scale changes, and can work in uncontrolled environments (cluttered backgrounds and lighting conditions). The superiority of our system is further demonstrated in two real-life HCI applications.

693 citations