Side-match Vector Quantization Research Articles

Reversible index-domain information hiding scheme based on side-match vector quantization

Information hiding has become an interesting topic that receives more and more attention. Recently, many hiding techniques were proposed to directly conceal secret information on an image. However, for convenience and efficiency, images are usually stored and compressed by lossy or lossless compression mechanisms in indices format. The hidden information might be erased or cancelled when the stego image is lossy compressed. Hence, this paper proposes an information hiding scheme based on side-match vector quantization (SMVQ), which conceals the secret information on the indices of the SMVQ compressed images. The proposed scheme not only can embed information in the indices of the compressed image with low image distortion, but also can recover the original indices to reconstruct the SMVQ compressed image. As the experimental results indicated, the proposed scheme indeed outperforms other schemes in terms of image quality, hiding capacity, and compression rate.

Adaptive Data Hiding Based on SMVQ Prediction

An adaptive data hiding scheme capable of hiding considerable quantities of secret data while preserving acceptable visual quality for cover images is proposed. The major idea of this scheme is to hide secret data into the compressed codes of cover image during the encoding process of side-match vector quantization (SMVQ) such that the interceptors will not capture the secret information. Based on the experimental results, it is confirmed that the proposed scheme is better than earlier works. Moreover, the receiver can efficiently receive both the compressed cover image and the hidden secret data at the same time.

An error resilient coding scheme for JPEG image transmission based on data embedding and side-match vector quantization

For an entropy-coded Joint Photographic Experts Group (JPEG) image, a transmission error in a codeword will not only affect the underlying codeword but also may affect subsequent codewords, resulting in a great degradation of the received image. In this study, an error resilient coding scheme for JPEG image transmission based on data embedding and side-match vector quantization (VQ) is proposed. To cope with the synchronization problem, the restart capability of JPEG images is enabled. The objective of the proposed scheme is to recover high-quality JPEG images from the corresponding corrupted images. At the encoder, the important data (the codebook index) for each Y (U or V) block in a JPEG image are extracted and embedded into another “masking” Y (U or V) block in the image by the odd–even data embedding scheme. At the decoder, after all the corrupted blocks within a JPEG image are detected and located, if the codebook index for a corrupted block can be correctly extracted from the corresponding “masking” block, the extracted codebook index will be used to conceal the corrupted block; otherwise, the side-match VQ technique is employed to conceal the corrupted block. Based on the simulation results obtained in this study, the performance of the proposed scheme is better than those of the five existing approaches for comparison. The proposed scheme can recover high-quality JPEG images from the corresponding corrupted images up to a block loss rate ( BLR) of 30%.

A Steganographic Method for Hiding Secret Data Using Side Match Vector Quantization

To increase the number of the embedded secrets and to improve the quality of the stego-image in the vector quantization (VQ)-based information hiding scheme, in this paper, we present a novel information-hiding scheme to embed secrets into the side match vector quantization (SMVQ) compressed code. First, a host image is partitioned into non-overlapping blocks. For these seed blocks of the image, VQ is adopted without hiding secrets. Then, for each of the residual blocks, SMVQ or VQ is employed according to the smoothness of the block such that the proper codeword is chosen from the state codebook or the original codebook to compress it. Finally, these compressed codes represent not only the host image but also the secret data. Experimental results show that the performance of the proposed scheme is better than other VQ-based information hiding scheme in terms of the embedding capacity and the image quality. Moreover, in the proposed scheme, the compression rate is better than the compared scheme.

New variable-rate finite state vector quantizer for image coding

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.

Smooth side-match weighted vector quantiser with variable block size for image coding

Although side-match vector quantisation (SMVQ) reduces the bit rate, the quality of image coding using SMVQ generally degenerates as the grey level transition across the boundaries of neighbouring blocks increases or decreases. The author proposes a smooth side-match weighted method to yield a state codebook according to the smoothness of the grey levels between neighbouring blocks. When a block is encoded, a corresponding weight is assigned to each neighbouring block to represent its relative importance using the smooth side-match weighted method. This smooth side-match weighted vector quantisation (SSMWVQ) achieves a higher PSNR than SMVQ at the same bit rate. Also, each block can be pre-encoded in an image, allowing each encoded block to use all neighbouring blocks to yield the state codebook in SSMWVQ, rather than using only two neighbouring blocks, as in SMVQ. Moreover, SSMWVQ selects many high-detail blocks as basic blocks to enhance the coding quality, and merges many low-detail blocks into a larger one to reduce further the bit rate. Experimental results reveal that SSMWVQ has a higher PSNR and lower bit rate than other methods.

Adaptive bit rate encoding mechanism based on irregular sampling and side-match vector quantization

A novel image compression scheme based on the side match vector quantization (SMVQ) scheme is proposed. The goal of this scheme is to improve the image quality of the SMVQ scheme while keeping a low bit rate. The required bit rate of this scheme is variable, and the bit rate can be control by the proposed adaptive bit rate encoding mechanism. Three techniques are applied in this scheme: the local skewness of the image, the adaptive irregular sampling and the modified side-match distortion measurement to satisfy the variant encoding requirements of the various image contents. The experimental results show that the derailment is suppressed and the image quality is approximated to VQ and enhanced ~2 dB from SMVQ at the same bit rate. Furthermore, the proposed scheme supports the adaptive bit rate encoding approach, which enables it to solve the trade-off between the image quality and the encoding bit rate.

Side-match prediction encoding scheme for predefined vector quantization approximation rates

In this paper, a dynamic state codebook-based side-match vector quantization (DSMVQ) is presented. The super codebook generation procedure is extended to explore the characteristics of codewords and to determine the dynamic state codebook sizes. In encoding, the 'near' codeword from the super codebook is selected based on the side-match distortion measurement. The size of the state codebook is retrieved according to the 'near' codeword. Then, side-match prediction encoding is employed to find the appropriate codeword from the state codebook. The experimental results show that the proposed scheme provides a lower encoding bit rate than classified SMVQ (CSMVQ) and two-level CSMVQ, and maintains better image quality than SMVQ and three-sided SMVQ. Furthermore, the adaptive encoded image quality is obtained according to the predefined VQ approximation rate. The adaptive encoding provides a possible solution to the trade-off between the encoding bit rate and the image quality.

Adaptive initial blocks for improving gradient-match and side-match vector quantization of images

The selection of initial blocks is one of the important issues in finite-state vector quantizers (FSVQs) for images. Conventional FSVQs use blocks located at fixed positions as initial blocks, which then are coded by the codewords in the super codebook. We propose an adaptive-initial-block (AIB) scheme to determine the initial blocks for gradient-match (GM) and side-match (SM) vector quantizers, which are two significant classes of FSVQs. According to the next-state functions used in GMVQs and SMVQs, the blocks with large boundary matching errors in original VQs are selected as the initial blocks. The error propagation effects can be reduced, and the coding performance is significantly improved. The simulation results show that the peak signal-to-noise ratios of coded images can be 2 dB higher than that of conventional GMVQs and SMVQs when the state codebook sizes are much smaller than that of the super codebook.

Side-match tree-structured vector quantiser for image progressive coding

The balanced tree-structured vector quantiser is the traditional method of achieving image progressive coding. During image progressive coding, an image is decoded step-by-step in a decoder. The author proposes an unbalanced tree-structured vector quantiser to perform image progressive coding for a given series of rate thresholds. Side-match vector quantisation and its variants have been proposed to reduce the bit rate in image coding. The tree-structured vector quantiser and the side-match vector quantiser are combined to perform image progressive coding, achieving a better coding quality than that obtained using only the tree-structured vector quantiser at the same bit rate.

Gradient match and side match fractal vector quantizers for images

In this paper, we propose gradient match fractal vector quantizers (GMFVQs) and side match fractal vector quantizers (SMFVQs), which are two classes of finite state fractal vector quantizers (FSFVQs), for the image coding framework. In our previous work, we proposed the noniterative fractal block coding (FBC) technique to improve the decoding speed and the coding performance for conventional FBC techniques. To reduce the number of bits for denoting the fractal code of the range block, the concepts of the gradient match vector quantizers (GMVQs) and the side match vector quantizers (SMVQs) are employed to the noniterative FBC technique. Unlike ordinary vector quantizers, the super codebooks in the proposed GMFVQs and SMFVQs are generated from the affine-transformed domain blocks in the noniterative FBC technique. The codewords in the state codebook are dynamically extracted from the super codebook with the side-match and gradient-match criteria. The redundancy in the affine-transformed domain blocks is greatly reduced and the compression ratio can be significantly increased. Our simulation results show that 15%-20% of the bit rates in the noniterative FBC technique are saved by using the proposed GMFVQs.

Smooth side-match classified vector quantizer with variable block size

Although the side-match vector quantizer (SMVQ) reduces the bit rate, the image coding quality by SMVQ generally degenerates as the gray level transition across the boundaries of the neighboring blocks is increasing or decreasing. This study presents a smooth side-match method to select a state codebook according to the smoothness of the gray levels between neighboring blocks. This method achieves a higher PSNR and better visual perception than SMVQ does for the same bit rate. Moreover, to design codebooks, a genetic clustering algorithm that automatically finds the appropriate number of clusters is proposed. The proposed smooth side-match classified vector quantizer (SSM-CVQ) is thus a combination of three techniques: the classified vector quantization, the variable block size segmentation and the smooth side-match method. Experimental results indicate that SSM-CVQ has a higher PSNR and a lower bit rate than other methods. Furthermore, the Lena image can be coded by SSM-CVQ with 0.172 bpp and 32.49 dB in PSNR.

Gradient match vector quantizers for images

We propose a new class of finite-state vector quantizers (FSVQs), called gradient match vector quantizers (GMVQs), in the im- age coding framework. GMVQs can be considered more general than previously proposed side match vector quantizers (SMVQs) since more border pixels between two neighboring blocks are considered in the state codebook design. Moreover, the concept of gradient matching used in GMVQs can be adjoined to another previously proposed overlap match vector quantizers (OMVQs). Thus another new class of FSVQs are pro- posed and named as gradient and overlap match vector quantizers (GOMVQs). GMVQs and GOMVQs utilize the 2-D spatial continuity of the pixel gradient as well as the high spatial correlation of pixels in typical grayscale images. Both minimize the gradient errors of the border pixels between blocks in ordinary vector quantization of images. In addition to reducing the granular noise that causes the annoying effect of visible pixel block boundaries, the proposed GMVQs and GOMVQs also can preserve the global gradient among the block boundaries and reduces more step noise than SMVQs and OMVQs in the area of high contrast edges. Experiments with the ''Lena'' image show that the proposed GMVQ can achieve the performance superior to those of SMVQ maxi- mum more than 1 dB peak signal-to-noise ratio (PSNR) under the same bit rate. On the other hand, GOMVQs can achieve further bit rate reduc- tion (about 0.14 to 0.3 bit/pixel) than OMVQ using the variable length noiseless code for the channel symbols. © 2000 Society of Photo-Optical Instru- mentation Engineers. (S0091-3286(00)00108-2)

Smooth side-match and smooth overlap-match vector quantizers for image coding

The side-match vector quantizer (SMVQ) is a well known method for image coding. We propose the smooth side-match vector quantizer (SSMVQ). The SSMVQ improves coding quality by making smoother the transition of the gray levels of the adjacent blocks. More- over, the smooth overlap-match vector quantizer (SOMVQ) is proposed to improve the performance of the overlap-match vector quantizer (OMVQ). Experimental results reveal that SSMVQ and SOMVQ have the higher peak SNR (PSNR) values, better visual perception quality, and lower bit rate than SMVQ and overlap-match vector quantizer (OMVQ), respectively. © 2000 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(00)03407-3)

Image Coding Using Adaptive Classified Side-Match Finite-State Vector Quantization

In this article, a new vector quantization (VQ) scheme called adaptive classified side-match finite-state vector quantization (ACSMVQ) is presented. This technique takes advantage of classified vector quantization (CVQ) and side-match vector quantization (SMVQ) so that the compression efficiency is much more attractive than traditional VQ techniques. In ACSMVQ, image blocks are classified into two main classes: edge blocks and non-edge blocks. For improving image quality, non-edge and edge blocks are further reclassified into different classes based on the properties of neighbouring blocks. Kirsh operators are utilized for the detection of edge properties within an image block. In order to synchronize the states in the encoder and decoder, the quantized upper and left blocks are exploited for classifying the input block both in the encoder and decoder. Therefore, no class index needs to be transmitted to the decoder. Moreover, the compression ratio can be further improved by applying small state code book. As shown in the experiment results, the average improvement of ACSMVQ over ordinary SMVQ is up to 2.7 dB at nearly the same bit-rate. Moreover, in comparison with ordinary VQ, the average improvement can be up to 4.24 dB at the same bit-rate.

Pattern-based side match vector quantization for image compression

The contiguous pixels are often similar in images. Hence side match vector quantization (SMVQ) can use the contiguous blocks to predict the other blocks. SMVQ has, however, a derailment problem that the error of the current state may result in errors in the next states. To remedy this problem, the pattern-based side match vector quantization (PSMYQ) is used in this paper. Unlike SMVQ, PSMVQ partitions an image into many patterns first. A pattern is a group of n × n blocks. Next, in each pattern, PSMVQ seeds one or more seed blocks, whose corresponding codewords are found by the full search method and employs the side match method to predict the residual blocks. The experimental results show that PSMVQ has three advantages, namely (a) that it is a faster method to encode an image than SMVQ, (b) that it has the ability to reduce the bit rate of an image effectively and (c) that the reconstructed image has a high image quality.

Three-sided side match finite-state vector quantization

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.

Reconstruction of lost blocks using codeword estimation

In previous years, error recovery on image compression methods, especially on VQ, has been an intensive research field. This paper presents a new pixel-based image reconstruction method (PIR) for recovering the lost blocks caused by transmission errors over noisy channels. Since there are high statistical correlations between the neighboring pixels of an image, PIR reconstructs the lost pixels using codebook search according to their adjacent pixels. In addition, an accelerating technique to shorten the execution time of the reconstruction is also proposed. The experimental results have shown that our PIR algorithm can efficiently and effectively recover the lost pixels. Moreover, if the image is complex or the size of the lost block is large, PIR is superior to the previous work, side-match vector quantization (SMVQ). Besides, unlike SMVQ, which is only suitable for the VQ encoding, PIR could be applied for any image compression algorithms such as JPEG, DCT and so on.

A new image coding algorithm using variable-rate side-match finite-state vector quantization

A new side-match vector quantizer, NewSMVQ, is presented in this paper. Three techniques are incorporated to improve the image quality, encoding speed, and bit rate for compressing images. The experimental result shows: i) the encoding time of NewSMVQ is almost 7 times faster than that of SMVQ (ordinary fixed-rate side-match vector quantizer) and CSMVQ (variable-rate SMVQ) and ii) NewSMVQ outperforms SMVQ and CSMVQ in terms of bit rate versus image quality tradeoffs.

Adaptive edge-based side-match finite-state classified vector quantization with quadtree map

Vector quantization (VQ) is an effective image coding technique at low bit rate. The side-match finite-state vector quantizer (SMVQ) exploits the correlations between neighboring blocks (vectors) to avoid large gray level transition across block boundaries. A new adaptive edge-based side-match finite-state classified vector quantizer (classified FSVQ) with a quadtree map has been proposed. In classified FSVQ, blocks are arranged 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 16 classes. Each class uses a master codebook that is different from the codebooks of other classes. In our experiments, results are given and comparisons are made between the new scheme and ordinary SMVQ and VQ coding techniques. As is shown, the improvement over ordinary SMVQ is up to 1.16 dB at nearly the same bit rate, moreover, the improvement over ordinary VQ can be up to 2.08 dB at the same bit rate for the image, Lena. Further, block boundaries and edge degradation are less visible because of the edge-vector classification. Hence, the perceptual image quality of classified FSVQ is better than that of ordinary SMVQ.