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Given the significant industrial growth of demand for virtual reality (VR), 360º video streaming is one of the most important VR applications that require cost-optimal solutions to achieve widespread proliferation of VR technology. Because of its inherent variability of data-intensive content types and its tiled-based encoding and streaming, 360º video requires new encoding ladders in adaptive streaming...
This paper develops a general framework of image retrieval, named A3, by introducing an auxiliary set of samples (object references), each of which is annotated with semantic attributes (tags). Given a query image (without tags), we first map it into the references by a non-convex sparse coding formulation, which jointly optimizes appearance reconstruction of the query and semantics consistency among...
In recent years, deep discriminative models have achieved extraordinary performance on supervised learning tasks, significantly outperforming their generative counterparts. However, their success relies on the presence of a large amount of labeled data. How can one use the same discriminative models for learning useful features in the absence of labels? We address this question in this paper, by jointly...
With the steady increase of offered cloud storage services, they became a popular alternative to local storage systems. Beside several benefits, the usage of cloud storage services can offer, they have also some downsides like potential vendor lock-in or unavailability. Different pricing models, storage technologies and changing storage requirements are further complicating the selection of the best...
Dictionary learning algorithms have received widespread acceptance when it comes to data analysis and signal representation problems. However, most existing algorithms assume isotropic noise. This is a restrictive assumption as the noise across samples may be nonuniform in a number of real world application. The aim of this article is to propose a sequential dictionary learning algorithm for measurement...
Image denoising is an extensively studied problem and can be used in many applications. In this paper, we propose an image denoising method using GMM and gradient sparse priors. GMM is a generic model and usually built based on image patches. Despite the high likelihood of GMM for image patches, the model is learned from image patches only. The relationship of neighboring patches is not learned. The...
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme...
Learning to hash has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval, due to its computation efficiency and retrieval quality. Deep learning to hash, which improves retrieval quality by end-to-end representation learning and hash encoding, has received increasing attention recently. Subject to the ill-posed gradient difficulty in the optimization with...
Convolutional sparse coding (CSC) plays an essential role in many computer vision applications ranging from image compression to deep learning. In this work, we spot the light on a new application where CSC can effectively serve, namely line drawing analysis. The process of drawing a line drawing can be approximated as the sparse spatial localization of a number of typical basic strokes, which in...
To compress large datasets of high-dimensional descriptors, modern quantization schemes learn multiple codebooks and then represent individual descriptors as combinations of codewords. Once the codebooks are learned, these schemes encode descriptors independently. In contrast to that, we present a new coding scheme that arranges dataset descriptors into a set of arborescence graphs, and then encodes...
In this paper, a genetic algorithm based converter system design procedure is presented with the feature of speed and universal. This method can better pack a high power density power converter.
Virtual reality (VR) applications target high-quality and zero-latency scene navigation to provide users with a full-immersion sensation within a scene. From a network perspective, this requires transmission of the omnidirectional content in its entirety, at a high resolution, which is not always feasible in bandwidth-limited networks. In this work, we propose an optimal transmission strategy for...
Efficient code descriptions of rate compatible polar codes are studied. An efficient code description method, so-called the polarization weight sequence that defines multiple codewords, was proposed in 3GPP standardization. We verify the validity of the method for various environments. We assess the performance degradation under successive cancellation (SC) decoding and SC list decoding compared with...
R-λ rate control scheme is recommended in the High Efficiency Video Coding (HEVC) standard, which shows high accuracy of bit rate control but lower rate distortion performance. In order to minimize the distortion subject to a target bit rate, a λ domain frame-level rate control scheme for low delay coding of HEVC was proposed. Firstly, an improved parameter updating method is presented for the frame...
To deal with the fastly increasing energy consumption and energy costs in the manufacturing process, manufacturing companies are forced to search some methods to reduce energy cost without affecting the yield of their products or sacrificing quality. In this paper, an extended job shop scheduling problem, where machines can work at different speeds with different energy consumption, is modified. A...
Nowadays, Rate-Distortion Optimization (RDO) is commonly used in hybrid video coding to maximize coding efficiency. Usually, the rate distortion tradeoff is explicitly computed in offline encoder implementations whereas R(D) model are used in live encoders to select the best decisions at a lower computational cost. For sake of simplicity, this (mathematical) modelling is often performed for each coding...
Wavelet neural network has a slow convergence rate, weak global search capability and easy to search the search results to a minimum, while the genetic algorithm has a high degree of parallelism, randomness, adaptive search and global optimization. The wavelet neural network is transformed and transformed to obtain the discretized wavelet neural network. In this paper, the three-layer wavelet neural...
The bit mapping pattern has a crucial effect on the error performance of a bit-interleaved polar-coded modulation (BIPCM) system. In this paper, we have shown that a large proportion of the mapping patterns are producing the same error performances and are hence redundant as far as mapping optimization is concerned. An effective method is then proposed to eliminate the redundant mapping patterns and...
A parameter optimization scheme of block interleaver for polar coding with bit-interleaved coded modulation (BICM) is proposed in this paper. We analyze the characteristics of a random interleaver applied to polar codes with high-order modulation, which is ideal but infeasible to implement. Then we derive the parameter optimization algorithm of block interleaver for polar coding with BICM. The block...
In this paper, a mobile robot path planning algorithm based on the rearrangement of gene is proposed for genetic algorithm and applied to solve the problem of mobile robot path planning. Firstly, it needs to build the robot path with the multi-plane model, and the genetic algorithm is used to search the optimal or sub optimal path. Then, with a new algorithm for the route of quadratic optimization,...
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