Codebook

About: Codebook is a research topic. Over the lifetime, 8492 publications have been published within this topic receiving 115995 citations.

A fast LBG codebook training algorithm for vector quantization

Abstract: A fast codebook training algorithm based on the Linde, Buzo and Gray (1980) LBG algorithm is proposed. The fundamental goal of this method is to reduce the computation cost in the codebook training process. In this method, a kind of mean-sorted partial codebook search algorithm is applied to the closest codeword search. At the same time, a generalized integral projection model is developed for the generation of test conditions, which are used to speed up the search process in finding the closest codeword for each training vector. With this proposed method, a significant time reduction can be achieved by avoiding the computation of unnecessary codewords. Our simulation results show that a significant reduction in computation cost is obtained with this proposed method. Besides, this method provides a flexible way of selecting the test conditions to accommodate the different image training sets.

Unsupervised and supervised visual codes with restricted boltzmann machines

Abstract: Recently, the coding of local features (e.g. SIFT) for image categorization tasks has been extensively studied. Incorporated within the Bag of Words (BoW) framework, these techniques optimize the projection of local features into the visual codebook, leading to state-of-the-art performances in many benchmark datasets. In this work, we propose a novel visual codebook learning approach using the restricted Boltzmann machine (RBM) as our generative model. Our contribution is three-fold. Firstly, we steer the unsupervised RBM learning using a regularization scheme, which decomposes into a combined prior for the sparsity of each feature's representation as well as the selectivity for each codeword. The codewords are then fine-tuned to be discriminative through the supervised learning from top-down labels. Secondly, we evaluate the proposed method with the Caltech-101 and 15-Scenes datasets, either matching or outperforming state-of-the-art results. The codebooks are compact and inference is fast. Finally, we introduce an original method to visualize the codebooks and decipher what each visual codeword encodes.

Low Complexity Receiver for Uplink SCMA System via Expectation Propagation

Abstract: Sparse code multiple access (SCMA) scheme is considered to be one promising non-orthogonal multiple access technology for the future fifth generation (5G) communications. Due to the sparse nature, message passing algorithm (MPA) has been used at the receiver to achieve close to maximum likelihood (ML) detection performance with much lower complexity. However, the complexity order of MPA is still exponential with the size of codebook and the degree of signal superposition on a given resource element. In this paper, we propose a novel low complexity iterative receiver based on expectation propagation algorithm (EPA), which reduces the complexity order from exponential to linear. Simulation results demonstrate that the proposed EPA receiver achieves nearly the same block error rate (BLER) performance as the conventional message passing algorithm (MPA) receiver with orders less complexity.

An on-line universal lossy data compression algorithm via continuous codebook refinement. I. Basic results

Abstract: A new on-line universal lossy data compression algorithm is presented. For finite memoryless sources with unknown statistics, its performance asymptotically approaches the fundamental rate distortion limit. The codebook is generated on the fly, and continuously adapted by simple rules. There is no separate codebook training or codebook transmission. Candidate codewords are randomly generated according to an arbitrary and possibly suboptimal distribution. Through a carefully designed "gold washing" or "information-theoretic sieve" mechanism, good codewords and only good codewords are promoted to permanent status with high probability. We also determine the rate at which our algorithm approaches the fundamental limit.

A Limited Feedback Joint Precoding for Amplify-and-Forward Relaying

Abstract: This paper deals with the practical precoding design for a dual hop downlink with multiple-input multiple-output (MIMO) amplify-and-forward relaying. First, assuming that full channel state information (CSI) of the two hop channels is available, a suboptimal dual hop joint precoding scheme, i.e., precoding at both the base station and relay station, is investigated. Based on its structure, a scheme of limited feedback joint precoding using joint codebooks is then proposed, which uses a distributed codeword selection to concurrently choose two joint precoders such that the feedback delay is considerably decreased. Finally, the joint codebook design for the limited feedback joint precoding system is analyzed, and results reveal that independent codebook designs at the base station and relay station using the conventional Grassmannian subspace packing method is able to guarantee that the overall performance of the dual hop joint precoding scheme improves with the size of each of the two codebooks. Simulation results show that the proposed dual hop joint precoding system using distributed codeword selection scheme exhibits a rate or BER performance close to the one using the optimal centralized codeword selection scheme, while having lower computational complexity and shorter feedback delay.