Codebook

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

Adaptive Huffman coding of 2-D DCT coefficients for image sequence compression

Abstract: This paper presents a new approach to adaptive Huffman coding of 2-D DCT coefficients for image sequence compression. Based on the popular motion-compensated interframe coding, the proposed method employs self-switching multiple Huffman codebooks for entropy coding of quantized transform coefficients. Unlike the existing multiple codebook approaches where the type of block (intra/inter or luminance/chrominance) selects a codebook, the proposed method jointly utilizes the type of block, the quantizer step size, and the zigzag scan position for the purpose of codebook selection. In addition, as another utilization of the quantizer step size and the scan position, the proposed method uses a variable-length “Escape” sequence for encoding rare symbols. Experimental results show that the proposed method with two codebooks provides 0.1–0.4 dB improvement over the single-codebook scheme and this margin turns out to be substantially larger than that the MPEG-2, two-codebook approach has over the single-codebook approach.

Method and system for an alternating channel delta quantizer for mimo pre-coders with finite rate channel state information feedback

Abstract: Aspects of a method and system for an alternating channel delta quantizer for MIMO pre-coders with finite rate channel state information feedback may include quantizing a change in channel state information in a MIMO pre-coding system onto a codebook using a cost function and selecting the codebook from a set of codebooks; and generating the set of codebooks from a plurality of codebooks, where each may comprise one or more unitary matrices. The channel state information may be a matrix V that may be generated using Singular Value Decomposition (SVD) and/or Geometric Mean Decomposition (GMD). The selecting of the codebooks may be enabled by alternating between the elements of the set of codebooks. The cost function f(A) may be defined by the following relationship: f  ( A ) = ( 1 N  ∑ j = 1 N   a jj  2 ) where A is a matrix of size N by N and a ij is element (i,j) of matrix A.

Codebook and feedback design for high order mimo

Abstract: Aspects of the present disclosure relate to techniques that may be utilized in networks with base stations and/or mobile devices that use large number of antennas or multi-dimensional arrays of antennas. According to certain aspects, a method for wireless communications is provided. The method may be performed, for example, by a base station and generally includes mapping N physical antennas arranged in at least two dimensions to K virtual antennas, wherein K is less than N, transmitting reference signals (RS) via the K virtual antennas, and receiving, from a user equipment, feedback based on the RS transmitted on the K virtual antennas.

Concealment of frame erasures and method

Abstract: A decoder for code excited LP encoded frames with both adaptive and fixed codebooks; erased frame concealment uses repetitive excitation plus a smoothing of pitch gain in the next good frame, plus multilevel voicing classification with multiple thresholds of correlations determining linear interpolated adaptive and fixed codebook excitation contributions.

BSIFT: Toward Data-Independent Codebook for Large Scale Image Search

Abstract: Bag-of-Words (BoWs) model based on Scale Invariant Feature Transform (SIFT) has been widely used in large-scale image retrieval applications. Feature quantization by vector quantization plays a crucial role in BoW model, which generates visual words from the high- dimensional SIFT features, so as to adapt to the inverted file structure for the scalable retrieval. Traditional feature quantization approaches suffer several issues, such as necessity of visual codebook training, limited reliability, and update inefficiency. To avoid the above problems, in this paper, a novel feature quantization scheme is proposed to efficiently quantize each SIFT descriptor to a descriptive and discriminative bit-vector, which is called binary SIFT (BSIFT). Our quantizer is independent of image collections. In addition, by taking the first 32 bits out from BSIFT as code word, the generated BSIFT naturally lends itself to adapt to the classic inverted file structure for image indexing. Moreover, the quantization error is reduced by feature filtering, code word expansion, and query sensitive mask shielding. Without any explicit codebook for quantization, our approach can be readily applied in image search in some resource-limited scenarios. We evaluate the proposed algorithm for large scale image search on two public image data sets. Experimental results demonstrate the index efficiency and retrieval accuracy of our approach.