Codebook

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

Sparse codebooks for the quantization of nondominant sub-bands in image coding

Abstract: A full-motion video coder based on a threedimensional subband framework is developed. The original image data is decomposed into different spatial-temporal frequency bands and based on the characteristics of the data in each band, different quantization strategies are applied to each band. The lowest spatial-temporal frequency band is coded using a three dimensional switched predictor and optimum scalar quantizer. The nondominant subbands which contain perceptually significant data are coded using a vector quantization approach in which the sparse, highly structured nature of the data in the higher-frequency subbands is exploited in the design of the codebook. >

Universal source coding with codebook transmission

Abstract: A universal source coding system with vector quantizer codebook transmissions is studied using high resolution quantization theory. Conditions are derived for the optimal tradeoff between quantizer resolution and the information rate used to transmit codebooks. A formula that tightly bounds the mean squared error of the universal coding system as a function of the time between codebook transmissions is experimentally verified and found to be tight, and a new and simpler derivation is given. Other research in the literature has proposed vector quantizing the transmitted codebooks; one conclusion we prove here is that under some reasonable conditions uniform scalar quantization of the transmitted codebooks performs as well as vector quantizing them. Experimental results are given that support the analytic derivations. >

Human computer interaction using isolated-words speech recognition technology

Abstract: This research paper aims to develop an isolated-word automatic speech recognition (IWASR) system based on vector quantization (VQ). This system receives, analyzes, searches and matches an input speech signal with the trained set of speech signals which are stored in the database/codebook, and returns matching results to users. IWASR is meant to assist customers calling a universitypsilas telephone operator to respond to their enquiries in a convenient way using their natural speech. Callers are assisted to select language, faculty and the staff name they wish to contact. To extract features from speech signals, Mel-frequency cepstral coefficients (MFCC) algorithm was applied. Subsequently, vector quantization was used for all feature vectors generated from the MFCC. A codebook was resulted from training the VQ initial codebook and experimental results showed that the recognition rate has been improved with the increase of codebook size and showed that the codebook size of 81 feature vectors had a recognition rate exceeded 85%.

Robust codebook-based video background subtraction

Abstract: Dynamic backgrounds and sudden illumination changes are two of the major problems associated with background subtraction techniques. In this paper, we present a novel approach to background subtraction that addresses both of these challenges. In particular, we present an improved codebook background modelling and subtraction technique. We utilise image segmentation on the background image and model the background with a codebook for each pixel along with a pseudo background layer. We perceive background motion as an occlusion of one background layer by a nearby background layer. In other words, sliding of one background layer over a neighbouring layer causes background motion and will hence result in false segmentation. We present our approach of codeword spreading across layer boundaries to handle background motion. Furthermore, we present a two-step update of the background codebook to handle both sudden and gradual illumination changes.

Three-sided side match finite-state vector quantization

Abstract: Several low bit-rate still-image compression methods have been presented, such as SPHIT, hybrid VQ, and the Wu-Chen (see Proc. IEEE ICASSP, 1997) method. In particular, the image "Lena" can be compressed using less than 0.15 bpp at 31.4 dB or higher. These methods exercise the analysis techniques (wavelet or subband) before distributing the bit rate to each piece of an image, thus the dilemma between bit rate and distortion can be solved. In this paper, we propose a simple but comparable method that adopts the technique of side match VQ only. The side match vector quantization (SMVQ) is an effective VQ coding scheme at low bit-rate. The conventional side match (two-sided) VQ utilizes the codeword information of two neighboring blocks to predict the state codebook of an input vector. We propose a hierarchical three-sided side match finite-state vector quantization (HTSMVQ) method that can: (1) make the state codebook size as small as possible-the size is reduced to one if the prediction can perform perfectly; (2) improve the prediction quality for edge blocks; and (3) regularly refresh the codewords to alleviate the error propagation of side match. In the simulation results, the image "Lena" can be coded with a PSNR 34.682 dB at 0.25 bpp. It is better than SPIHT, EZW, FSSQ and hybrid VQ with 34.1, 33.17, 33.1, and 33.7 dB, respectively. At a bit rate lower than 0.15 bpp, only the enhanced version of EZW performs better than our method, at about 0.14 dB.