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In order to study the relationship between an underwater shock wave and jet thrust oscillation, a simulation model of underwater jet is established. As per a numerical calculation based on the SST (Shear Stress Transport), and VOF (Volume of Fluid) models, the flow speed of jet gas has been changed with the pressure ratio. When the jet speed becomes supersonic, its corresponding shock structure will occur. When the pressure ratio is small, the shock wave structure in the nozzle is normal, and in case of an increase of the jet speed, the jet is moving from the nozzle inside to the outside. When the pressure ratio reaches a certain number, the shock is out of nozzle, and the nozzle is operating in an over-expansion state, the thrust oscillation amplitude is high. When the pressure ratio is large, and the nozzle is operating in the under-expanded state, then the structure of shock wave is very complex, jet back-attack no longer appears, and the thrust amplitude is rather smaller than the over-expanded one. Two kinds of situations are compared. And the comparison revealed that the highly under-expanded state is more conducive to the stability of the underwater vehicle, which can provide a reference for the corresponding design.

The gaseous jet of the solid rocket motor running under water is influenced by both gravity and buoyancy, which will have a significant influence on the flow field structure and thrust of the motor, especially in the initial period when vehicles are launching horizontally. The work in this paper consists of three parts: firstly, to understand the mechanism of the thrust oscillation characteristics and the jet structure of a solid rocket motor running under water, as affected by gravity and buoyancy, a 3-d numerical simulation using the volume of fluid (VOF) model was established. Compared with those results neglecting gravity and buoyancy, conclusions were obtained that the jet structure, considering gravity and buoyancy, are more consistent with the experimental results. Secondly, the principle of momentum was used to analyze the flow field structure and the thrust oscillating characteristics in the initial period of operation. Finally, after analyzing the density gradient along the axis of the motor, results indicated that the length of jets under the influence of gravity and buoyancy varies linearly and the slope of the line is related to the working conditions. Comparing the trajectories under different conditions, a common theme emerged. The laws of thermodynamics are also used to simulate the jets as gas-water-vapor, three-phase, systems. The effect of phase transitions on the structure of jets and the characteristics of their thrust form the key conclusions.

This paper proposes an efficient content adaptive screen image scaling scheme for the real-time screen applications like remote desktop and screen sharing. In the proposed screen scaling scheme, a screen content classification step is first introduced to classify the screen image into text and pictorial regions. Afterward, we propose an adaptive shift linear interpolation algorithm to predict the new pixel values with the shift offset adapted to the content type of each pixel. The shift offset for each screen content type is offline optimized by minimizing the theoretical interpolation error based on the training samples respectively. The proposed content adaptive screen image scaling scheme can achieve good visual quality and also keep the low complexity for real-time applications., Comment: ICIP 2013

We study how to learn multiple dictionaries from a dataset, and approximate any data point by the sum of the codewords each chosen from the corresponding dictionary. Although theoretically low approximation errors can be achieved by the global solution, an effective solution has not been well studied in practice. To solve the problem, we propose a simple yet effective algorithm \textit{Group $K$-Means}. Specifically, we take each dictionary, or any two selected dictionaries, as a group of $K$-means cluster centers, and then deal with the approximation issue by minimizing the approximation errors. Besides, we propose a hierarchical initialization for such a non-convex problem. Experimental results well validate the effectiveness of the approach., Comment: The developed algorithm is similar with "Christopher F. Barnes, A new multiple path search technique for residual vector quantizers, 1994", but we conduct the research independently and apply it in data/feature compression and image retrieval

The emerging cloud gaming technology has been growing fast, driving up huge mobile consumer demands. The video streaming based cloud gaming scenario renders the game scenes in the cloud servers, and streams the encoded sequences to the thin clints where the game scenes are decoded and displayed to the players. However, current existing clouding gaming services have some problems, such as the latency and bandwidth limitation. The size of the video stream is usually quite large which requires heavy transmission. Worse still, the frame data rate will burst when the game scenes contain fast translation or rotation, resulting in strong latency problem. In this paper, we propose a novel video streaming based cloud gaming algorithm which reduces the burst of the frame rate significantly. There are mainly two innovations in this paper. Firstly, based on the analysis of the motion estimation strategy in the video codec, we introduce image homography technique for better motion prediction. Meanwhile, according to the rasterization rules of the game engine, we present a special designed interpolation algorithm named Edge Preserved Interpolation (EPI), for more accurate edge interpolation and further reduce the residues in the edge regions. The proposed algorithm is implemented on the x264 platform. Experimental results show that our algorithm has 18.0% BD-rate reduction compared with x264.

The emerging cloud gaming technology has been growing fast, driving up huge mobile consumer demands. The video streaming based cloud gaming scenario renders the game scenes in the cloud servers, and streams the encoded sequences to the thin clints where the game scenes are decoded and displayed to the players. However, current existing clouding gaming services have some problems, such as the latency and bandwidth limitation. The size of the video stream is usually quite large which requires heavy transmission. Worse still, the frame data rate will burst when the game scenes contain fast translation or rotation, resulting in strong latency problem. In this paper, we propose a novel video streaming based cloud gaming algorithm which reduces the burst of the frame rate significantly. There are mainly two innovations in this paper. Firstly, based on the analysis of the motion estimation strategy in the video codec, we introduce image homography technique for better motion prediction. Meanwhile, according to the rasterization rules of the game engine, we present a special designed interpolation algorithm named Edge Preserved Interpolation (EPI), for more accurate edge interpolation and further reduce the residues in the edge regions. The proposed algorithm is implemented on the x264 platform. Experimental results show that our algorithm has 18.0% BD-rate reduction compared with x264.

The emerging cloud gaming technology has been growing fast, driving up huge mobile consumer demands. The video streaming based cloud gaming scenario renders the game scenes in the cloud servers, and streams the encoded sequences to the thin clints where the game scenes are decoded and displayed to the players. However, current existing clouding gaming services have some problems, such as the latency and bandwidth limitation. The size of the video stream is usually quite large which requires heavy transmission. Worse still, the frame data rate will burst when the game scenes contain fast translation or rotation, resulting in strong latency problem. In this paper, we propose a novel video streaming based cloud gaming algorithm which reduces the burst of the frame rate significantly. There are mainly two innovations in this paper. Firstly, based on the analysis of the motion estimation strategy in the video codec, we introduce image homography technique for better motion prediction. Meanwhile, according to the rasterization rules of the game engine, we present a special designed interpolation algorithm named Edge Preserved Interpolation (EPI), for more accurate edge interpolation and further reduce the residues in the edge regions. The proposed algorithm is implemented on the x264 platform. Experimental results show that our algorithm has 18.0% BD-rate reduction compared with x264.

Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named Optimized Cartesian $K$-Means (OCKM), to better encode the data points for more accurate approximate nearest neighbor search. In OCKM, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life datasets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search., Comment: to appear in IEEE TKDE, accepted in Apr. 2014

Due to the divergent characteristics of image contents and text contents in screen videos, how to make the joint optimization leveraging rate-distortion (R-D) optimized block classification and bit allocation is critical to the compression performance. In this paper, a general model-based solution is proposed as an attempt to solve this problem. The contributions of this paper are twofold: First, the rate and distortion characteristics of image blocks and text blocks in block-based content-adaptive screen video encoder (BASC) are carefully studied, and the rate and distortion models are proposed. Second, with the proposed rate and distortion models, the R-D optimized block classification and bit allocation are derived using bisection searched Lagrange multiplier method. Experimental results demonstrate that the proposed R-D optimized block classification and bit allocation algorithms are able to adapt to diverse screen contents, which results in a significant gain of up to 4.5dB in PSNR. © 2013 IEEE.

This paper describes a handheld video browser that in corporates shot boundary detection, key frame extraction, semantic content analysis, key frame browsing, and similarity search.

Due to the divergent characteristics of image contents and text contents in screen videos, how to make the joint optimization leveraging rate-distortion (R-D) optimized block classification and bit allocation is critical to the compression performance. In this paper, a general model-based solution is proposed as an attempt to solve this problem. The contributions of this paper are twofold: First, the rate and distortion characteristics of image blocks and text blocks in block-based content-adaptive screen video encoder (BASC) are carefully studied, and the rate and distortion models are proposed. Second, with the proposed rate and distortion models, the R-D optimized block classification and bit allocation are derived using bisection searched Lagrange multiplier method. Experimental results demonstrate that the proposed R-D optimized block classification and bit allocation algorithms are able to adapt to diverse screen contents, which results in a significant gain of up to 4.5dB in PSNR. © 2013 IEEE.

In this paper, we propose a new data structure for approximate nearest neighbor search. This structure augments the neighborhood graph with a bridge graph. We propose to exploit Cartesian concatenation to produce a large set of vectors, called bridge vectors, from several small sets of subvectors. Each bridge vector is connected with a few reference vectors near to it, forming a bridge graph. Our approach finds nearest neighbors by simultaneously traversing the neighborhood graph and the bridge graph in the best-first strategy. The success of our approach stems from two factors: the exact nearest neighbor search over a large number of bridge vectors can be done quickly, and the reference vectors connected to a bridge (reference) vector near the query are also likely to be near the query. Experimental results on searching over large scale datasets (SIFT, GIST and HOG) show that our approach outperforms state-of-the-art ANN search algorithms in terms of efficiency and accuracy. The combination of our approach with the IVFADC system also shows superior performance over the BIGANN dataset of $1$ billion SIFT features compared with the best previously published result.

$K$-means, a simple and effective clustering algorithm, is one of the most widely used algorithms in multimedia and computer vision community. Traditional $k$-means is an iterative algorithm---in each iteration new cluster centers are computed and each data point is re-assigned to its nearest center. The cluster re-assignment step becomes prohibitively expensive when the number of data points and cluster centers are large. In this paper, we propose a novel approximate $k$-means algorithm to greatly reduce the computational complexity in the assignment step. Our approach is motivated by the observation that most active points changing their cluster assignments at each iteration are located on or near cluster boundaries. The idea is to efficiently identify those active points by pre-assembling the data into groups of neighboring points using multiple random spatial partition trees, and to use the neighborhood information to construct a closure for each cluster, in such a way only a small number of cluster candidates need to be considered when assigning a data point to its nearest cluster. Using complexity analysis, image data clustering, and applications to image retrieval, we show that our approach out-performs state-of-the-art approximate $k$-means algorithms in terms of clustering quality and efficiency.

The $k$-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct $k$-NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to construct approximate $k$-NN graphs with emphasis in: efficiency and accuracy. We hierarchically and randomly divide the data points into subsets and build an exact neighborhood graph over each subset, achieving a base approximate neighborhood graph; we then repeat this process for several times to generate multiple neighborhood graphs, which are combined to yield a more accurate approximate neighborhood graph. Furthermore, we propose a neighborhood propagation scheme to further enhance the accuracy. We show both theoretical and empirical accuracy and efficiency of our approach to $k$-NN graph construction and demonstrate significant speed-up in dealing with large scale visual data.

Tagging is nowadays the most prevalent and practical way to make images searchable. However, in reality many manually-assigned tags are irrelevant to image content and hence are not reliable for applications. A lot of recent efforts have been conducted to refine image tags. In this paper, we propose to do tag refinement from the angle of topic modeling and present a novel graphical model, regularized Latent Dirichlet Allocation (rLDA). In the proposed approach, tag similarity and tag relevance are jointly estimated in an iterative manner, so that they can benefit from each other, and the multi-wise relationships among tags are explored. Moreover, both the statistics of tags and visual affinities of images in the corpus are explored to help topic modeling. We also analyze the superiority of our approach from the deep structure perspective. The experiments on tag ranking and image retrieval demonstrate the advantages of the proposed method.

In this paper, we address a problem of managing tagged images with hybrid summarization. We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images. We propose a novel approach, called homogeneous and heterogeneous message propagation ($\text{H}^\text{2}\text{MP}$). Similar to the affinity propagation (AP) approach, $\text{H}^\text{2}\text{MP}$ reduce the conventional \emph{vector} message propagation to \emph{scalar} message propagation to make the algorithm more efficient. Beyond AP that can only handle homogeneous data, $\text{H}^\text{2}\text{MP}$ generalizes it to exploit extra heterogeneous relations and the generalization is non-trivial as the reduction to scalar messages from vector messages is more challenging. The main advantages of our approach lie in 1) that $\text{H}^\text{2}\text{MP}$ exploits visual similarity and in addition the useful information from the associated tags, including the associations relation between images and tags and the relations within tags, and 2) that the summary is both visually and semantically satisfactory. In addition, our approach can also present a textual summary to a tagged image collection, which can be used to automatically generate a textual description. The experimental results demonstrate the effectiveness and efficiency of the roposed approach.

This paper describes a handheld video browser that in corporates shot boundary detection, key frame extraction, semantic content analysis, key frame browsing, and similarity search.

This paper deals with the optimal packet loss protection issue for streaming the fine granularity scalable (FGS) video bitstreams over IP networks. Unlike many other existing protection schemes, we develop an error-resilient unequal error protection (ER-UEP) method that adds redundant information optimally for loss protection and, at the same time, cancels completely the dependency among bitstream after loss recovery. In our ER-UEP method, the FGS enhancement-layer bitstream is first packetized into a group of independent and scalable data packets. Parity packets, which are also scalable, are then generated. Unequal protection is finally achieved by properly shaping the data packets and the parity packets. We present an algorithm that can optimally allocate the rate budget between data packets and parity packets, together with several simplified versions that have lower complexity. Compared with conventional UEP schemes that suffer from bit contamination ( caused by the bit dependency within a bitstream), our method guarantees successful decoding of all received bits, thus leading to strong error-resilience ( at any fixed channel bandwidth) and high robustness ( under varying and/or unclean channel conditions). Copyright (C) 2006 Hindawi Publishing Corporation. All rights reserved.

This paper deals with the optimal packet loss protection issue for streaming the fine granularity scalable (FGS) video bitstreams over IP networks. Unlike many other existing protection schemes, we develop an error-resilient unequal error protection (ER-UEP) method that adds redundant information optimally for loss protection and, at the same time, cancels completely the dependency among bitstream after loss recovery. In our ER-UEP method, the FGS enhancement-layer bitstream is first packetized into a group of independent and scalable data packets. Parity packets, which are also scalable, are then generated. Unequal protection is finally achieved by properly shaping the data packets and the parity packets. We present an algorithm that can optimally allocate the rate budget between data packets and parity packets, together with several simplified versions that have lower complexity. Compared with conventional UEP schemes that suffer from bit contamination ( caused by the bit dependency within a bitstream), our method guarantees successful decoding of all received bits, thus leading to strong error-resilience ( at any fixed channel bandwidth) and high robustness ( under varying and/or unclean channel conditions). Copyright (C) 2006 Hindawi Publishing Corporation. All rights reserved.

This paper deals with the optimal packet loss protection issue for streaming the fine granularity scalable (FGS) video bitstreams over IP networks. Unlike many other existing protection schemes, we develop an error-resilient unequal error protection (ER-UEP) method that adds redundant information optimally for loss protection and, at the same time, cancels completely the dependency among bitstream after loss recovery. In our ER-UEP method, the FGS enhancement-layer bitstream is first packetized into a group of independent and scalable data packets. Parity packets, which are also scalable, are then generated. Unequal protection is finally achieved by properly shaping the data packets and the parity packets. We present an algorithm that can optimally allocate the rate budget between data packets and parity packets, together with several simplified versions that have lower complexity. Compared with conventional UEP schemes that suffer from bit contamination ( caused by the bit dependency within a bitstream), our method guarantees successful decoding of all received bits, thus leading to strong error-resilience ( at any fixed channel bandwidth) and high robustness ( under varying and/or unclean channel conditions). Copyright (C) 2006 Hindawi Publishing Corporation. All rights reserved.

This paper addresses the problem of optimal packetization for Fine Granularity Scalability (FGS) bit stream. A very general performance metric for streaming FGS bit streams over packet erasure networks is first defined and analyzed. Then three packetization strategies, namely the baseline packetization, the binary-tree packetization and the optimal packetization are presented, justified and compared. Finally, some experimental results are presented to demonstrate the effectiveness of the packetization schemes.

This paper addresses the problem of optimal packetization for Fine Granularity Scalability (FGS) bit stream. A very general performance metric for streaming FGS bit streams over packet erasure networks is first defined and analyzed. Then three packetization strategies, namely the baseline packetization, the binary-tree packetization and the optimal packetization are presented, justified and compared. Finally, some experimental results are presented to demonstrate the effectiveness of the packetization schemes.

This paper addresses the problem of optimal packetization for Fine Granularity Scalability (FGS) bit stream. A very general performance metric for streaming FGS bit streams over packet erasure networks is first defined and analyzed. Then three packetization strategies, namely the baseline packetization, the binary-tree packetization and the optimal packetization are presented, justified and compared. Finally, some experimental results are presented to demonstrate the effectiveness of the packetization schemes.