Side-Match Vector Quantization for Reconstruction of Lost Blocks

Side-match vector quantization is a finite-state technique for image coding. This research shows that the side-match vector quantization is an error-propagating code and it is similar to a catastrophic convolutional code. Here, we propose a Viterbi-based algorithm to solve this problem. Various noise detection algorithms are integrated into the Viterbi algorithm (to yield the Viterbi-based algorithm) for a much better performance. According to the simulation of a binary symmetric channel with random bit-error rate (BER) 0.1%-0.01%, the Viterbi-based algorithm provides 2.8-9.6 dB and 2.3-8.4 dB gain compared with the conventional side-match vector quantization decoder and the Viterbi decoder, respectively. In addition, the proposed algorithm requires much fewer computations than the Viterbi algorithm.

Vector quantization (VQ) is an effective image coding technique at low bit rate. Side-match finite-state vector quantizer (SMVQ) exploits the correlations between the neighboring blocks (vectors) to avoid large gray level transition across block boundaries. In this paper, a new adaptive quadtree-based side-match finite-state vector quantizer (QBSMVQ) has been proposed. In QBSMVQ, the blocks are classified into two main classes, edge blocks and nonedge blocks, to avoid selecting a wrong state codebook for an input block. In order to improve the image quality, edge vectors are reclassified into sixteen classes. Each class uses a master codebook that is different from the codebook of other classes. In our experiments, results are given and comparisons are made between the new scheme and ordinary SMVQ coding techniques. As will be shown, the improvement of QBSMVQ over the ordinary SMVQ is up to 3.13 dB at nearly the same bit rate. Moreover, the improvement over the ordinary VQ can be up to 4.30 dB at the same bit rate for the image Lena. Further, block boundaries and edge degradation are less visible because of edge-vector classification. Hence, the perceived improvement in quality over ordinary SMVQ is even greater for human sight.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A variable-rate mean-match correlation vector quantizer, MMCVQ, is presented as an alternative vector quantizer of side-match vector quantizer (SMVQ) for fast image encoding. Before encoding, the mean values of all codewords are computed, then a sorted codebook is obtained according to the ascending order of the mean values of codewords. During the encoding stage, high correlation of the adjacent image blocks and mean values of the current processing vector and codewords are considered to obtain the state-codebook of the current processing vector. The experimental result shows: (1) the encoding time of MMCVQ is almost 7 times faster than that of full-search VQ and almost 42 times faster than that of SMVQ, and (2) compared with SMVQ, the encoding quality will increase more than 1 dB in PSNR, and (3) the bit rate of MMCVQ is smaller than that of SMVQ by 0.03-0.05 b/pixel.

Side-match vector quantization is an attractive finite-state coding technique for images. This research shows that the side-match vector quantization is similar to catastrophic code where infinite number of decoding errors is caused by a finite number of channel errors. A 1D side- match vector quantization is first proposed to limit the error propagation across different row of blocks when the channel is noisy. Then three tests are proposed for noisy rows detection with minimum overhead information. These tests include the 1D side-match vector quantization decoding test, double-row test, and triple-row test. Finally, a modified Viterbi algorithm is proposed to decode the images encoded by the 1D side-match vector quantization.

Although side-match vector quantization (SMVQ) and its variants have been proposed to reduce the bit rate of coding images, the coding quality is decreased by it. The reason is that when a block is encoded in SMVQ, the side-match method uses only two neighboring blocks, (no edge information is available in the other two neighboring blocks) to predict the state of the codebook. We propose a new variable-rate finite state vector quantizer (VRFSVQ) to encode the blocks with a variable rate according to both the coding quality and the bit rate. The VRFSVQ can preencode all the blocks, allowing each block to employ more than two neighboring blocks to yield the state codebook. Furthermore, whereas users of SMVQ must specify the size of the state codebook before applying the side-match method to yield the state codebook, in the VRFSVQ the users do not need to specify the size of the state codebook for each encoding block. Experimental results indicate that the VRFSVQ has a high peak SNR and low bit rate for image coding.

Recently, the information security issues are watched closely. Numerous researchers have exploited data hiding to develop secure communications. This study proposes a lossless data hiding scheme for side match vector quantization (SMVQ) compressed images based on the search order coding (SOC) algorithm. In the SMVQ index table, the difference between the current index and one of its adjacent SMVQ indices is calculated and encoded into an indicator generated from the Huffman coding where the codeword length is variable. The indicator also contains some bits to identify different embedding cases. The experimental results indicate that our proposed scheme can achieve a high compression ratio.

Over the years, data hiding has been seen as a useful tool to protect users' privacy. In this paper, a novel reversible data hiding scheme, which utilizes improved SMVQ (Side-match Vector Quantization) indices and embeds privacy data with higher counts indices, is proposed for color images. As a result, it improves Yang's scheme, which employs half of the VQ indices in codebook to embed privacy data, and achieves a good performance in data hiding. In addition, by employing the correlation information, block structure similarity measurement (BSSM) of reference channel, the distribution of SMVQ indices is enhanced. Experimental results indicate that our proposed scheme enhances the prediction of SMVQ, resulting in a more concentrated distribution of indices. After embedding privacy data into the indices with higher count and encoding all the indices based on the distribution, better compression rate and embedding efficiency are successfully obtained. Furthermore, extended experiments demonstrate that the proposed scheme outperforms other related reversible data hiding schemes as well.

In this paper, a novel highly efficient lossless data hiding scheme is proposed to achieve the goal of hiding secret data into vector quantization (VQ)-compressed images that can be losslessly reconstructed after the secret data is extracted in the decoder. For VQ-compressed images, the index-modifying and the side-match VQ (SMVQ) techniques can be applied to encode indices and hide secret data. In general, data hiding accompanied by the SMVQ technique can yield a higher embedding capacity and a lower bit rate, yet more time consuming. In contrast, data hiding accompanied by the index-modifying technique can yield a lower embedding capacity and a higher bit rate, yet less time consuming. To possess the advantages of the two techniques while removing the shortcomings, the neighboring processed indices are employed to speed up the processes of generating state codebooks required for encoding and hiding. To evaluate the effectiveness of this approach, various test images are employed in the experiments. As documented in the experimental results, it is shown that the performance of the proposed scheme is superior to former schemes in terms of compression ratio, embedding rate, processing efficiency, and embedding capacity.

Information hiding refers to concealing data into cover media to prevent the private message from being stolen in peer-to-peer networks, mobile communication, and the cloud environment. Vector quantization (VQ) provides one means for this purpose; a distortion is created between the raw media and VQ compressed media. In this study, we proposed a flexible side-match vector quantization (SMVQ) information-hiding method. By using this method, the private message can be embedded into the distortion, and the visual quality of the stego media is improved. The proposed method achieves an improvement of about 13.13% (i.e., +3.73 dB) in the peak signal-to-noise ratio of the stego image and an improvement of about 59,355 bits of embedding capacity on average compared with Wang et al.’s method. The experiment results verify the significant contributions of this study to the field of SMVQ-based information hiding, especially with regard to the image quality of the stego image and the embedding capacity.

Reversible joint fingerprinting and decryption based on side match vector quantization

Competitive learning algorithms for vector quantization

This paper investigates the development of language identification based on Mel-Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) algorithm. In this study, a total of ten speakers were chosen randomly with different languages from online language database. A total of six males and four females were selected as subjects for this research and each of them spoke different languages, including Arabic, Chinese, English, Korean and Malay. The MFCC will be extracted to derive the related feature vector. Vector Quantization (VQ) algorithm is then used as classifier. The recognition rate is then calculated for each language. Several experiments were conducted to find the optimum parameters, in which we found that sampling frequency of 16000 Hz and codebook size of 75 provided good results. On average, the recognition rate for all five languages evaluated was 78%. The experimental results show that our proposed system provides a good recognition rate.

Steganography is a tool which allows the data for transmission by concealing secret information in a tremendously growing network. In this paper, a novel technique quick response method (QRM) is proposed for the purpose of encryption and decryption. Existing system uses side match vector quantization (SMVQ) technique which has some challenges such as security issues and performance issues. To handle the security and performance issues, the proposed system uses two methods, namely, quick response method and shifting method. In the proposed system, encoding part calculates the performance for capacity, PSNR (peak signal‐to‐noise ratio), MSE (mean square error), and SSIM (structural similarity index method), and the decoding part calculates the performance of MSE (mean square error) and PSNR (peak signal‐to‐noise ratio). The shifting method is used to increase the data hiding capacity. In this system, the encryption part embeds the secret image using steganography and the decryption part extracts the original image. By analyzing and comparing the proposed system with the existing system, it is proved that the system proposed was much better than the existing systems.

In this paper, we propose a temporal dynamic index reordering vector quantization for wavelet-based embedded coding. Da Silva et al. introduced a vector quantization concept that is similar to EZW called a successive approximation vector quantizer (SAVQ). The successive refinement process is defined as a temporal process in our proposed algorithm. The temporal updates are performed in every refinement pass, and the updates of codevectors reflect the updates of angles for vector approximation. Because the approximation trajectory of SAVQ is similar to that of the least-mean- square algorithm, temporal redundancy does not seem to be obvious. However, the redundancy becomes more clear in the improved SAVQ. By carefully analyzing the angle transitions, we are successfully able to apply dynamic index reordering vector quantization (DIRVQ) in the temporal domain and improve the coding performance. Efficient encoding and decoding algorithms for D4 lattice vector quantization are also proposed, and the algorithms can be applicable to DIRVQ to reduce the computational complexity of the reordering process. Experiments are performed with the 2x2 vector size. For almost all tested bpp's the PSNR performance of our proposed algorithm for a lena image outperforms that of SAVQ up to 0.18 dB. Improvement is measured for the highly detailed baboon image as well.

This paper will describe the vector quantization (VQ) of speech spectrum parameters as used in state of the art real time speech coding algorithms. The complexity of these speech coding algorithms, combined with the constraints of real time operation, limit the practical size of the vector quantization tables. This conflicts with the requirement that large VQ tables are needed to provide high quality coded speech. The goal of this paper is to demonstrate the feasibility of using tree searched vector quantizers to overcome this conflict.