Rate-distortion and complexity optimized motion estimation for H.264 video coding

11.264 video coding standard supports several inter-prediction coding modes that use macroblock (MB) partitions with variable block sizes. Rate-distortion (R-D) optimal selection of both the motion vectors (MVs) and the coding mode of each MB is essential for an H.264 encoder to achieve superior coding efficiency. Unfortunately, searching for optimal MVs of each possible subblock incurs a heavy computational cost. In this paper, in order to reduce the computational burden of integer-pel motion estimation (ME) without sacrificing from the coding performance, we propose a R-D and complexity joint optimization framework. Within this framework, we develop a simple method that determines for each MB which partitions are likely to be optimal. MV search is carried out for only the selected partitions, thus reducing the complexity of the ME step. The mode selection criteria is based on a measure of spatiotemporal activity within the MB. The procedure minimizes the coding loss at a given level of computational complexity either for the full video sequence or for each single frame. For the latter case, the algorithm provides a tight upper bound on the worst case complexity/execution time of the ME module. Simulation results show that the algorithm speeds up integer-pel ME by a factor of up to 40 with less than 0.2 dB loss in coding efficiency.Publisher's Versio

Bayesian models and algorithms for protein beta-sheet prediction

Prediction of the three-dimensional structure greatly benefits from the information related to secondary structure, solvent accessibility, and non-local contacts that stabilize a protein's structure. Prediction of such components is vital to our understanding of the structure and function of a protein. In this paper, we address the problem of beta-sheet prediction. We introduce a Bayesian approach for proteins with six or less beta-strands, in which we model the conformational features in a probabilistic framework. To select the optimum architecture, we analyze the space of possible conformations by efficient heuristics. Furthermore, we employ an algorithm that finds the optimum pairwise alignment between beta-strands using dynamic programming. Allowing any number of gaps in an alignment enables us to model beta-bulges more effectively. Though our main focus is proteins with six or less beta-strands, we are also able to perform predictions for proteins with more than six beta-strands by combining the predictions of BetaPro with the gapped alignment algorithm. We evaluated the accuracy of our method and BetaPro. We performed a 10-fold cross validation experiment on the BetaSheet916 set and we obtained significant improvements in the prediction accuracy

Bayesian models and algorithms for protein beta-sheet prediction

Prediction of the three-dimensional structure greatly benefits from the information related to secondary structure, solvent accessibility, and non-local contacts that stabilize a protein's structure. Prediction of such components is vital to our understanding of the structure and function of a protein. In this paper, we address the problem of beta-sheet prediction. We introduce a Bayesian approach for proteins with six or less beta-strands, in which we model the conformational features in a probabilistic framework. To select the optimum architecture, we analyze the space of possible conformations by efficient heuristics. Furthermore, we employ an algorithm that finds the optimum pairwise alignment between beta-strands using dynamic programming. Allowing any number of gaps in an alignment enables us to model beta-bulges more effectively. Though our main focus is proteins with six or less beta-strands, we are also able to perform predictions for proteins with more than six beta-strands by combining the predictions of BetaPro with the gapped alignment algorithm. We evaluated the accuracy of our method and BetaPro. We performed a 10-fold cross validation experiment on the BetaSheet916 set and we obtained significant improvements in the prediction accuracy

Yetim proteinlerde ikincil yapı öngörüsü için eğitim kümesi indirgeme yöntemleri = Training set reduction methods for single sequence protein secondary structure prediction

Orphan proteins are characterized by the lack of significant sequence similarity to almost all proteins in the database. To infer the functional properties of the orphans, more elaborate techniques that utilize structural information are required. In this regard, the protein structure prediction gains considerable importance. Secondary structure prediction algorithms designed for orphan proteins (also known as single-sequence algorithms) cannot utilize multiple alignments or aligment profiles, which are derived from similar proteins. This is a limiting factor for the prediction accuracy. One way to improve the performance of a single-sequence algorithm is to perform re-training. In this approach, first, the models used by the algorithm are trained by a representative set of proteins and a secondary structure prediction is computed. Then, using a distance measure, the original training set is refined by removing proteins that are dissimilar to the initial prediction. This step is followed by the re-estimation of the model parameters and the prediction of the secondary structure. In this paper, we compare training set reduction methods that are used to re-train the hidden semi-Markov models employed by the IPSSP algorithm. We found that the composition based reduction method has the highest performance compared to the other reduction methods. In addition, threshold-based reduction performed bettern than the reduction technique that selects the first 80% of the dataset proteins

Frame-level complexity control in H.264 video coding

H.264 kodlama standardı, değişken blok boyutlu makroblok bölüntüleri kullanan çok sayıda farklı çerçeveler arası kestirim kipini desteklemektedir. Ne var ki, her olası bölüntü için eniyi devinim vektörlerini aramanın hesaplama masrafı çok yüksektir. Bu bildiride, her makroblok için hangi bölüntülerin en iyi olabileceğini tahmin eden orijinal bir çerçeve seviyesinde karmaşıklık kontrol algoritması öneriyoruz. Devinim vektörlerinin aranması sadece bu seçilen bölüntüler için yürütülmekte, dolayısıyla devinim kestirimi adımının karmaşıklığı azaltılabilmektedir. Kip seçim kriteri olarak makrobloğun uzay-zamansal etkinliğinin basit bir ölçütü kullanılmıştır. Yöntem, her çerçevede belli bir yürütüm süresi bütçesinin sağlanmasını kodlama verimliliğinde en az bir kayıpla garanti etmektedir. Benzetim sonuçları, algoritmanın, 0.2 dB’den az bir kodlama kaybıyla, tamsayı-piksel devinim kestirimini 40 kata kadar hızlandırdığını göstermektedir.H.264 video coding standard supports several interprediction coding modes that use macroblock partitions with variable block sizes. Unfortunately, searching for optimal motion vectors of each possible partition incurs a heavy computational cost. In this paper, we propose a novel frame-level complexity control algorithm that determines for each macroblock which partitions are likely to be optimal. Motion vector search is carried out for only the selected partitions, thus reducing the complexity of the motion estimation step. The mode selection criteria is based on a measure of spatio-temporal activity within the macroblock. For each frame, the procedure guarantees that an execution time budget is met with minimum loss of coding efficiency. Simulation results show that the algorithm speeds up integer-pel motion estimation by a factor of up to 40 with less than 0.2 dB loss in coding efficiency.Publisher's Versio

Rate-distortion and complexity joint optimization for fast motion estimation in H.264 video coding

H.264 video coding standard offers several coding modes including inter-prediction modes that use macroblock partitions with variable block sizes. Choosing a rate-distortion optimal mode among these possibilities contributes significantly to the superior coding efficiency of the H.264 encoder. Unfortunately, searching for optimal motion vectors of each possible subblock incurs a heavy computational cost. In this paper, in order to reduce the complexity of integer-pel motion estimation, we propose a rate-distortion and complexity joint optimization method that selects for each MB a subset of partitions to evaluate during motion estimation. This selection is based on simple measures of spatio-temporal activity within the MB. The procedure is optimized to minimize mode estimation error at a certain level of computational complexity. Simulation results show that the algorithm speeds up the motion estimation module by a factor of up to 20 with little loss in coding efficiency.This research was supported by the TUBITAK Grant 104E125Publisher's Versio

Face recognition with independent component based super-resolution

Performance of current face recognition algorithms reduces significantly when they are applied to low-resolution face images. To handle this problem, super-resolution techniques can be applied either in the pixel domain or in the face subspace. Since face images are high dimensional data which are mostly redundant for the face recognition task, feature extraction methods that reduce the dimension of the data are becoming standard for face analysis. Hence, applying superresolution in this feature domain, in other words in face subspace, rather than in pixel domain, brings many advantages in computation together with robustness against noise and motion estimation errors. Therefore, we propose new superresolution algorithms using Bayesian estimation and projection onto convex sets methods in feature domain and present a comparative analysis of the proposed algorithms with those already in the literature

Low complexity inter-mode selection for H.264

The coding efficiency of the H.264/AVC standard enables the transmission of high quality video over bandwidth limited networks. Due to the use of multiple Macroblock (MB) partitions, the Motion estimation module has extremely high complexity that makes it unpractical for most real-time applications on resource-limited platforms such as hand held devices. In this paper we propose a novel algorithm that significantly reduces the encoding complexity while maintaining high rate distortion performance. The proposed method reduces the Motion estimation (ME) computational complexity by accurately predicting the optimal MB partitions and restricting the number of candidate modes based on a-priori probabilities computed from spatio-temporal information. The experimental results show that the speed up of UmHexagonS [1] (one of the most efficient ME algorithms) can be doubled while maintaining the coding efficiency of Full Search.Publisher's Versio