6799009
On packetization of embedded multimedia bitstreams
We study the problem of packetizing embedded multimedia bitstreams to improve the error resilience of source (compression) codes. This problem is important because of the increasing popularity of embedded compression methodology and its suitability for scalable streaming media over IP or/and mobile IP. We study various packetization schemes against packet erasure at both low and high bit rates. Maximizing packetization efficiency for embedded bitstreams is formulated as a discrete optimization problem and globally optimal packetization (OP) algorithms are proposed under different settings. Suboptimal packetization algorithms are also devised to reduce the complexity of the OP algorithms. In order to assess their effectiveness, the proposed packetization algorithms are used to packetize embedded image and video bitstreams with simulated packet loss. Experimental results show that our OP algorithms slightly outperforms suboptimal ones. In addition to confirming the superiority of the OP algorithms, these results also provide justification of heuristic packetization methods published in the literature.
15084916
Computing the channel capacity and rate-distortion function with two-sided state information
In this correspondence, we present iterative algorithms that numerically compute the capacity-power and rate-distortion functions for coding with two-sided state information. Numerical examples are provided to demonstrate efficiency of our algorithms.
18292356
On dualities in multiterminal coding problems
It has been shown recently that under certain conditions there exist dualities between different multiterminal (MT) source and channel coding problems. Following these results, we study lossless MT source coding and deterministic MT channel coding problems and point out different dualities between them. In particular, we show that there exists a functional duality between a Slepian-Wolf (SW) coding problem and a deterministic broadcast channel (DBC) coding problem and between a lossless multiple-description (MD) coding problem and a deterministic multiple-access channel (DMAC) coding problem. In analogy to the duality established between DBC and DMAC coding problems, we further propose a similar duality between SW and lossless MD coding problems; in this way, we form a closed "duality loop" of four MT coding problems, which imposes the existence of a single common rate point in the achievable rate regions of all four dual problems. We also consider duality in zero-error MT coding and shed light on practical code design with an example. Finally, extension to the case with only one lossless/deterministic component in the source/channel coding problem is provided.
20227157
Trellis coded color quantization of images
We examine color quantization of images using trellis coded quantization (TCQ). Together with a simple dithering scheme, an 8-bit trellis coded color quantizer reproduces images that are visually indistinguishable from the 24-bit originals. The proposed algorithm can be viewed as a predictive trellis coded color quantization scheme. It is universal in the sense that no training or lookup table is needed. The complexity of TCQ is linear with respect to image size, making trellis coded color quantization suitable for interactive graphics and a window-based display environment.
8002270
Audio coding and image denoising based on the nonuniform modulated complex lapped transform
Xiong and Malvar recently introduced a nonuniform modulated complex lapped transform (NMCLT) with good time-localization and controllable frequency resolution by using an oversampled nonuniform filter bank to generate its real and the imaginary components. In this paper, we first show that oversampling in the NMCLT is not necessary in theory but a by-product of fast implementation in practice. We also point out that the amount of oversampling, which can be flexibly controlled, depends on the application. We then describe in detail the implementation of the inverse transform, which was not addressed clearly by Xiong and Malvar. We present the first applications of the NMCLT to audio coding and image denoising. A scalable audio coder has been implemented by controlling the amount of oversampling and exploiting redundancy among the NMCLT coefficients via predictive coding. Experimental results show that the audio coder reduces pre-echoes and improves the sound quality of audio clips with transient sounds. A simple denoising algorithm based on the NMCLT has also been devised to provide images with better visual quality than those obtained with wavelet-based soft thresholding.
15109463
Slepian-Wolf Coded Nested Lattice Quantization for Wyner-Ziv Coding: High-Rate Performance Analysis and Code Design
Nested lattice quantization provides a practical scheme for Wyner-Ziv coding. This paper examines the high-rate performance of nested lattice quantizers and gives the theoretical performance for general continuous sources. In the quadratic Gaussian case, as the rate increases, we observe an increasing gap between the performance of finite-dimensional nested lattice quantizers and the Wyner-Ziv distortion-rate function. We argue that this is because the boundary gain decreases as the rate of the nested lattice quantizers increases. To increase the boundary gain and ultimately boost the overall performance, a new practical Wyner-Ziv coding scheme called Slepian-Wolf coded nested lattice quantization (SWC-NQ) is proposed, where Slepian-Wolf coding is applied to the quantization indices of the source for the purpose of compression with side information at the decoder. Theoretical analysis shows that for the quadratic Gaussian case and at high rate, SWC-NQ performs the same as conventional entropy-coded lattice quantization with the side information available at both the encoder and the decoder. Furthermore, a nonlinear minimum mean-square error (MSE) estimator is introduced at the decoder, which is theoretically proven to degenerate to the linear minimum MSE estimator at high rate and experimentally shown to outperform the linear estimator at low rate. Practical designs of one- and two-dimensional nested lattice quantizers together with multilevel low-density parity-check (LDPC) codes for Slepian-Wolf coding give performance close to the theoretical limits of SWC-NQ
15145046
A novel approach to diagnose diabetes based on the fractal characteristics of retinal images
A novel diagnostic scheme to develop quantitative indexes of diabetes is introduced in this paper. The fractal dimension of the vascular distribution is estimated because we discovered that the fractal dimension of a severe diabetic patient's retinal vascular distribution appears greater than that of a normal human's. The issue of how to yield an accurate fractal dimension is to use high-quality images. To achieve a better image-processing result, an appropriate image-processing algorithm is adopted in this paper. Another important fractal feature introduced in this paper is the measure of lacunarity, which describes the characteristics of fractals that have the same fractal dimension but different appearances. For those vascular distributions in the same fractal dimension, further classification can be made using the degree of lacunarity. In addition to the image-processing technique, the resolution of original image is also discussed here. In this paper, the influence of the image resolution upon the fractal dimension is explored. We found that a low-resolution image cannot yield an accurate fractal dimension. Therefore, an approach for examining the lower bound of image resolution is also proposed in this paper. As for the classification of diagnosis results, four different approaches are compared to achieve higher accuracy. In this study, the fractal dimension and the measure of lacunarity have shown their significance in the classification of diabetes and are adequate for use as quantitative indexes.
9588683
Performance evaluation of an admission control algorithm: dynamic threshold with negotiation
An admission control algorithm for a multimedia server is responsible for determining if a new request can be accepted without violating the QoS requirements of the existing requests in the system. Most admission control algorithms treat every request uniformly and hence optimize the system performance by maximizing the number of admitted and served requests. In practice, requests might have different levels of importance to the system. Requests offering high contribution or reward to the system performance deserve priority treatment. Failure of accepting a high-priority request would incur high penalty to the system.A novel threshold-based admission control algorithm with negotiation for two priority classes of requests is proposed in our previous study. The server capacity is divided into three partitions based on the threshold values: one for each class of requests and one common pool shared by two classes of requests. Reward and penalty are adopted in the proposed system model. High-priority requests are associated with higher values of reward as well as penalty than low-priority ones. In this paper, given the characteristics of the system workload, the proposed analytical models aim to finds the best partitions, optimizing the system performance based on the objective function of the total reward minus the total penalty. The negotiation mechanism reduces the QoS requirements of several low-priority clients, by cutting out a small fraction of the assigned server capacity, to accept a new high-priority client and to achieve a higher net earning value. Stochastic Petri-Net model is used to find the optimal threshold values and two analytical methods are developed to find sub-optimal settings. The experiment results show that the sub-optimal solutions found by the proposed analytical methods are very close to optimal ones. The methods enable the algorithm to dynamically adjust the threshold values, based on the characteristics of the system workload, to achieve higher system performance.
1344352
Quality meshing for polyhedra with small angles
We present an algorithm to compute a Delaunay mesh conforming to a polyhedron possibly with small input angles. The radius-edge ratio ofmost output tetrahedra are bounded by a constant, except possibly those that are provably close to small angles. Further, the mesh is graded, that is, edge lengths are at least a constant fraction of the local feature sizes at the edge endpoints. Unlike a previous algorithm, this algorithm is simple to implement as it avoids computing local feature sizes and protective zones explicitly. Our experimental results confirm our claims and show that few skinny tetrahedra remain.
14292166
A self-adjusting quality of service control scheme
We propose and analyze a self-adjusting Quality of Service (QoS) control scheme with the goal of optimizing the system reward as a result of servicing different priority clients with varying workload, QoS and reward/penalty requirements. Our scheme is based on resource partitioning and designated "degrade QoS areas" such that system resources are partitioned into priority areas each of which is reserved specifically to serve only clients in a corresponding class with no QoS degradation, plus one "degraded QoS area" into which all clients can be admitted with QoS adjustment being applied only to the lowest priority clients. We show that the best partition is dictated by the workload and the reward/penalty characteristics of clients in difference priority classes. The analysis results can be used by a QoS manager to optimize the system total rewawrd dynamically in response to changing workloads at run time. We demonstrate the validity of our scheme by means of simulation and comparing the proposed QoS self-adjusting scheme with those that do not use resource partitioning or designated degraded QoS area.
1362947
Optimal Replication of Series-Parallel Graphs for Computation-Intensive Applications
Abstract We consider the replication problem of series-parallel (SP) task graphs where each task may run on more than one processor. The objective of the problem is to minimize the total cost of task execution and interprocessor communication. We call it the minimum cost replication problem for SP graphs (MCRP-SP). In this paper, we adopt a new communication model where the purpose of replication is to reduce the total cost. The class of applications we consider is computation-intensive applications in which the execution cost of a task is greater than its communication cost. The complexity of MCRP-SP for such applications is proved to be NP-complete. We present a branch-and-bound method to find an optimal solution as well as an approximation approach for a suboptimal solution. The numerical results show that such replication may lead to a lower cost than the optimal assignment problem (in which each task is assigned to only one processor) does. The proposed optimal solution has complexity O ( n 2 2 n M ), while the approximation solution has O ( n 4 M 2 ), where n is the number of processors in the system and M is the number of tasks in the graph.