Study on parallel approach in H.26L video encoder

The pattern design of superhydrophobic surfaces can be significantly aided by computations that predict the Cassie-Baxter (CB) to Wenzel (W) transition, which is responsible for the break-down of superhydrophobic behavior. We present a computational framework for the optimization of patterned surfaces based on the energy barriers of the CB-W transitions which comprises the following elements: (a) design of structured surface patterns, for example, arrays of pillars, with parameterized geometric features such as size, pitch, slope, and roundness. (b) Computation of the wetting states with a modified Young-Laplace equation that facilitates the introduction of solid/liquid interactions for complex surface patterns and has significantly lower computational cost than other commonly used methods, such as the volume-of-fluid, phase-field, and so forth. (c) Incorporation of the modified Young-Laplace in the simplified string method, allowing the calculation of the minimum energy paths of wetting transitions which, apart from the energy barriers, also reveal the transition mechanisms (CB failure modes). (d) Accommodation of large-scale problems with good parallel performance and scalability on multicore-distributed memory systems using fast iterative solvers and the Message Passing Interface communication protocol. We demonstrate the computational framework with a shape optimization study of inverted conical frustum pillars. The optimization objective function is the resistance to the CB-W transition, which is quantified by the energy barrier-a relatively large energy barrier suggests improved superhydrophobicity. We also report the parallel performance, in terms of parallel speedup for problems ranging from three hundred thousands to 12 million degrees of freedom, solved using up to 40 processing cores.

In this chapter, we will present an advanced parallel electro-cardiac simulator, which employs anisotropic and inhomogeneous conductivities in realistic three-dimensional geometries for modeling both the heart and the torso. Since partial differential equations (PDEs) constitute the main part of the mathematical model, this chapter thus demonstrates a concrete example of solving PDEs on parallel computers. It will be shown that good overall parallel performance relies on at least two factors. First, the serial numerical strategy must find a parallel substitute that is scalable with respect to both convergence and work amount. Second, care must be taken to avoid unnecessary duplicated local computations while maintaining an acceptable level of load balance.

A new video encoder proposal, H.26L, is compared against H.263 and H.263+. In the comparison, both computational complexity and compression performance are analyzed. Moreover, the trade-off possibilities between the complexity and compression performance within H.26L are presented. Experimental comparisons with H.263 and H.263+ show that H.26L reduces the output bit rate about 30% with the same quality. The computation time increases about three times compared to H.263 and leads into the encoding speed of 3-6 fps for QCIF sequences on a 400 MHz Pentium III processor. Realtime operation can be achieved by applying additional, algorithmic and platform-specific optimizations.

Today the great challenge to the IEEE 802.11 wireless LAN is to support real time and multimedia applications. However, the characteristics of wireless channel (low and fluctuating bandwidth link and the large error rate) post different problems in supporting the requirements on QoS (bandwidth, delays, jitters) of these sensitive applications. We focus on the JVC H.26L video encoder, the wireless LAN and their interaction in QoS. The interaction is made by the guarantee of a reliable H.26L video transport over the IEEE 802.11 wireless LAN. In this context, we propose a novel cross layer architecture. This architecture is based on a marking scheme, which exploits the new propositions of the IEEE 802.11 E group for supporting QoS.

We present fast and scalable parallel computations for a number of important and fundamental matrix problems on distributed memory systems (DMS). These problems include computing the powers, the inverse, the characteristic polynomial, the determinant, the rank, the Krylov matrix, and an LU- and a QR-factorization of a matrix, and solving linear systems of equations. These parallel computations are based on efficient implementations of the fastest sequential matrix multiplication algorithm on DMS. We show that compared with the best known time complexities on PRAM, our parallel matrix computations achieve the same speeds on distributed memory parallel computers (DMPC), and have an extra polylog factor in the time complexities on DMS with hypercubic networks. Furthermore, our parallel matrix computations are fully scalable on DMPC and highly scalable over a wide range of system size on DMS with hypercubic networks. Such fast and scalable parallel matrix computations were not seen before on any distributed memory systems.

A real-time frame-layer rate control algorithm is proposed for low bit rate video coding over IEEE 802.11 wireless local area network (WLAN). The proposed rate control method performs bit allocation at the frame level to minimize the average distortion over an entire sequence as well as variations in distortion between frames. A new frame-layer rate-distortion model is derived, and a non-iterative optimization method is used for low computational complexity. The proposed algorithm produces low encoding time delay, and is suitable for real-time low-complexity video encoder. Experimental results indicate that the proposed control method provides better visual and PSNR performance than the existing TMN8 rate control method.

Many studies have shown that there is a direct relationship between Single Nucleotide Polymorphisms (SNPs) and the appearance of complex diseases, such as Alzheimer’s or Parkinson’s. However, recent advances in the Study of the Complete Genome Association indicate that the relationship between SNPs and these diseases goes beyond a simple one-to-one relationship, that is, the appearance of multiple SNPs (epistasis) influences the appearance of these diseases. In this sense, this work proposes the application of the NSGA-II multi-objective algorithm for the detection of epistasis of multiple loci in a database with 31,341 SNPs. Moreover, a parallel study has been performed to reduce the execution time of this problem. Our implementation not only achieves a reasonable good parallel performance and scalability, but also its biological significance overcomes other approaches published in the literature.

A new feedforward control method to control encod-ing parameters is proposed for low bit rate video encoding. At low transmission bit rates, all input video frames cannot be transmitted and an encoding frame interval varies with the information amount in each encoded frame. Subjective video quality is evaluated from both picture degradation and encoding frame intervals. Because picture degradation and encoding frame intervals are traded off, encoding parameter control is very important. The proposed method is based on a hybrid transform predictive coding scheme with adaptive coefficient selection. The method estimates the amount of information for some encoding parameters and selects parameters so that given control characteristics can be achieved. Computer simulations for both the proposed method and a conventional feedback method show the effectiveness of the new method. This method will be applied to an NETEC series low bit rate video codec.

Graphics Processing Units (GPU) are becoming a widespread tool for general-purpose scientific computing, and are attracting interest for future onboard satellite image processing payloads due to their ability to perform massively parallel computations. This paper describes the GPU implementation of an algorithm for onboard lossy hyperspectral image compression, and proposes an architecture that allows to accelerate the compression task by parallelizing it on the GPU. The selected algorithm was amenable to parallel computation owing to its block-based operation, and has been optimized here to facilitate GPU implementation incurring a negligible overhead with respect to the original single-threaded version. In particular, a parallelization strategy has been designed for both the compressor and the corresponding decompressor, which are implemented on a GPU using Nvidia's CUDA parallel architecture. Experimental results on several hyperspectral images with different spatial and spectral dimensions are presented, showing significant speed-ups with respect to a single-threaded CPU implementation. These results highlight the significant benefits of GPUs for onboard image processing, and particularly image compression, demonstrating the potential of GPUs as a future hardware platform for very high data rate instruments.

We introduce a low-complexity, high-performance DCT-based video coding algorithm that is designed for very low bit rates. The algorithm employs a fast statistical and rate-distortion-optimized motion estimation method with integer-pel accuracy. The searching is constrained to a small diamond-shaped area whose center is the statistical mean, which is obtained using a finite-state machine modeling method based on dynamic state vector quantization. The same modeling method is also employed for coding the motion vectors. A simple rate-distortion criterion is used for alternating between three modes of operation for each 8/spl times/8 block: motion-only coding, motion-compensated predictive coding, and intra coding. Experimental results indicate that the proposed algorithm can outperform current H.263-based video coders significantly at very low bit rates. But, more important is that our algorithm's rate, distortion, and complexity can be easily controlled, a feature desired in many very low bit rate video communication applications due to power and mobility constraints.

We propose a new real-time rate-distortion (R-D) optimized dynamic frame-skipping (DFS) transcoder for low bit rate video transmission. The proposed transcoder can adjust the number of skipped frames according to the target frame rate as well as the channel rate. Experimental results indicate that the proposed R-D optimized transcoder can not only perform frame-skipping effectively but also improve visual quality in the low bit rate video transcoding.

There are several ways to represent 3D scene information. One popular way is based on N-view plus N-depth representation. In applications based on this representation, efficient compression of both view and depth is important. In this paper, we present a depth coding method that uses depth downsampling and a novel depth upsample filter that uses the color view in the depth upsampling. Our method of depth down- and up-sampling is able to reconstruct clear object boundaries in the upsampled depth maps, and, therefore, we can obtain a better coding efficiency. Our experimental results show that the proposed depth resampling filter, used in combination with a standard state-of-the-art video encoder, can increase both the coding efficiency and rendering quality, particularly at lower bit rates.

The paper presents an analysis of the suitability of the architecture of dynamic SMP clusters with communication on the fly for massively parallel fine grain numerical computations. It is assumed that the proposed architecture is implemented using the highly modular "system on chip" and "network on chip" technology. This technology is considered to provide soon a very large number of co-operating processors embedded in a single parallel system, thus enabling massively parallel computations. The proposed architecture of dynamic clusters with communication on the fly meets requirements of large scale fine grain computations and can be successfully applied in this technology. Experimental simulation results are presented concerning efficiency of fine grain parallel implementation of a typical numerical problem which is matrix multiplication based on recursive data decomposition. Selection of optimal parallel computation grain is discussed. Estimations of the efficiency of the proposed methods for fine grain computations for large problem size are presented

A fundamental issue in real-time interactive voice transmissions over unreliable IF networks is the loss or late arrival of packets for playback. This problem is especially serious when transmitting low bit rate-coded speech with pervasive dependencies introduced. In this case, the loss or late arrival of a single packet will lead to the loss of subsequent dependent frames. We study end-to-end loss-concealment schemes for ensuring high quality in playback. We propose a novel multiple description-coding method for concealing packet losses in transmitting low bit rate-coded speech. Based on high correlations observed in linear predictor parameters-in the form of Line Spectral Pairs (LSPs)-of adjacent frames, we generate multiple descriptions in senders by interleaving LSPs, and reconstruct lost LSPs in receivers by linear interpolations. As excitation codewords have low correlations, we further enlarge the segment size for excitation generation and replicate excitation codewords in all the descriptions in order to maintain the same transmission bandwidth. Our proposed scheme can be extended easily to more than two descriptions and can adapt its number of descriptions dynamically to network-loss conditions. Experimental results on FS-1016 CELP, ITU G.723.1, and FS MELP coders show good performance of our scheme.

Graph edit distance (GED) is a measure for quantifying the similarity between two graphs. Because of its flexibility and versatility, GED is widely used in many real applications. However, the main disadvantage of GED is its high computational cost. Many solutions have been proposed to speed up GED computation, but most of them focus on developing serial algorithms and only very few solutions consider parallel computing. In this paper, we study a parallel GED computation to elaborate a fast and precise algorithm. Unlike existing solutions that utilize either a depth-first or a best-first search, we propose a hybrid approach that combines depth-first and best-first search paradigms. Our approach can quickly find tighter GED upper bounds, and effectively prune the search space using the upper bounds. Based on the approach, we develop an efficient parallel GED computation algorithm named ${\sf HGED}$ . To maximize thread utility, ${\sf HGED}$ is also equipped with a novel dynamic load balancing scheme whose main focus is on reducing the overhead of thread synchronization. Experimental results on widely used real datasets show that, on average, ${\sf HGED}$ outperforms the state-of-the art serial algorithm ${\sf AStar}^{+}$ - ${\sf LSa}$ by 6 times and the state-of-the parallel algorithm ${\sf PGED}$ by 4 times.