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Codebook

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


Papers
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Proceedings ArticleDOI
13 Jun 2010
TL;DR: The learning problem is formulated as a convex quadratic programming and adopted alternating optimization to solve it efficiently and improves the performance, in particular in the case where the number of training examples is not sufficient for large size codebook.
Abstract: This paper presents a codebook learning approach for image classification and retrieval. It corresponds to learning a weighted similarity metric to satisfy that the weighted similarity between the same labeled images is larger than that between the differently labeled images with largest margin. We formulate the learning problem as a convex quadratic programming and adopt alternating optimization to solve it efficiently. Experiments on both synthetic and real datasets validate the approach. The codebook learning improves the performance, in particular in the case where the number of training examples is not sufficient for large size codebook.

59 citations

Patent
22 Oct 2013
TL;DR: In this paper, a method for receiving a reference CSI configuration and a following CSI configuration information which is configured to report a same RI (Rank Indicator) as the reference CSI information is presented.
Abstract: The present invention relates to a method for receiving a reference CSI configuration information and a following CSI configuration information which is configured to report a same RI (Rank Indicator) as the reference CSI configuration information, receiving a first precoding codebook subset information for the reference CSI configuration information and a second precoding codebook subset information for the following CSI configuration information, set of RIs according to the second precoding codebook subset information is same as set of RIs according to the first precoding codebook subset information, and transmitting CSI determined based on at least one of the first precoding codebook subset information and the second precoding codebook subset information.

59 citations

Journal ArticleDOI
TL;DR: This paper proposes a low complexity codebook-based beamforming scheme that consists of multiple levels and level-adaptive antenna selection in order to reduce the beamforming setup time and shows from the numerical results that the proposed scheme can provide an effective SNR gain and an average spectral efficiency approaching those of the codebooks with exhaustive search.
Abstract: In this paper, we consider a beamforming system in millimeter-wave wireless personal area networks (WPAN), where transmit and receive weight vectors are jointly derived by exchanging a training sequence repeatedly with different combinations of transmit and receive weight vectors. We propose a low complexity codebook-based beamforming scheme that consists of multiple levels and level-adaptive antenna selection in order to reduce the beamforming setup time. For each level, 1) the transmit and receive antennas are selected according to pre-determined inter-element spacings, 2) the training sequences are sent with different weight vectors from a pre-defined codebook and 3) the receiver selects the best transmit and receive weight vectors in order to optimize an effective signal-to-noise ratio (SNR) and these vectors are used to determine the codebook for the next following level. Even with low complexity, we show from the numerical results that our proposed scheme can provide an effective SNR gain and an average spectral efficiency approaching those of the codebook-based beamforming with exhaustive search.

59 citations

Journal ArticleDOI
TL;DR: This paper extends and modifies classified vector quantization (CVQ) to improve the quality of compressed images and shows that the image quality is improved dramatically.

59 citations

Journal ArticleDOI
TL;DR: The Sparse Regression Code is robust in the following sense: for any ergodic source, the proposed encoder achieves the optimal distortion-rate function of an i.i.d Gaussian source with the same variance.
Abstract: We propose computationally efficient encoders and decoders for lossy compression using a sparse regression code. The codebook is defined by a design matrix and codewords are structured linear combinations of columns of this matrix. The proposed encoding algorithm sequentially chooses columns of the design matrix to successively approximate the source sequence. It is shown to achieve the optimal distortion-rate function for independent identically distributed (i.i.d) Gaussian sources under the squared-error distortion criterion. For a given rate, the parameters of the design matrix can be varied to tradeoff distortion performance with encoding complexity. An example of such a tradeoff as a function of the block length $n$ is the following. With computational resource (space or time) per source sample of $O((n/\log n)^{2})$ , for a fixed distortion-level above the Gaussian distortion-rate function, the probability of excess distortion decays exponentially in $n$ . The sparse regression code is robust in the following sense: for any ergodic source, the proposed encoder achieves the optimal distortion-rate function of an i.i.d Gaussian source with the same variance. Simulations show that the encoder has good empirical performance, especially at low and moderate rates.

59 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023217
2022495
2021237
2020383
2019432
2018364