Vector Quantization For Compression Research Articles

Reversible Data Hiding Scheme Based on VQ Prediction and Adaptive Parametric Binary Tree Labeling for Encrypted Images

In this paper, we propose a reversible data hiding scheme for the encrypted images (RDHEI) based on vector quantization (VQ) prediction and parametric binary tree labeling (PBTL). VQ compression is a lossy image compression method, the difference between the original image and the decompressed image is small when the length of codebook is sufficient. Thus, VQ can be applied as a tool for pixel value prediction. Based on VQ prediction, PBTL method is applied to label the embeddable and non-embeddable pixels. Through adaptive setting of parameters, the modified PBTL can provide optimal pixel labeling strategies and thus maximize the overall embedding capacity. Furthermore, the VQ index and the secret data are stream ciphered to avoid leakage of the image content and secret information. Different metrics are used to show that the marked encrypted images are highly secure. In comparison with several state-of-the-art schemes, our scheme outperforms the related works in embedding rate for two commonly applied image databases. In addition, extraction of the secret data and recovery of the original image can be operated separately according to authorization.

Adaptive Radiographic Image Compression Technique using Hierarchical Vector Quantization and Huffman Encoding

The objective of this paper is to provide a proposed method for radiographic image compression with maximum Compression Ratio (CR) as possible and keeping all details, especially in the Region Of Interest (ROI) that contains the important information in the image. In the proposed method; firstly, the ROI is separated from the image background using an automatic threshold based on the occurrence histogram of the variance image, then the image background is compressed with the maximum possible compression ratio using image pyramid compression followed by lossy Vector Quantization (VQ) compression technique based on Generalized Lloyd Algorithm (GLA) method for generating the codebook. After that, ROI is compressed using the Huffman Code (HC) with low compression ratio and with minimum loss in details. Finally, the compressed image is obtained by combing both the compressed background and the compressed ROI. The results are evaluated by calculating the Normalized Cross Correlation (NCC) and the Structural Similarity Index (SSIM) between the original image and the recovered image after decompression at different compression ratios. The obtained results are compared with the results obtained from compressing the whole image without separation using lossy VQ, HC, Discrete Cosine Transform (DCT), and JPEG2000 compression methods. The results show that, the proposed method owns more reliable performance than completely compressing the radiographic image without separation using VQ compression or Huffman coding compression.

Transmission of side-scan sonar snippets from an underway unmanned underwater vehicle

An unique vector quantization compression methodology was applied to compress and encode side-scan sonar snippets of mine-like objects generated by automated target recognition tools on-board underway unmanned underwater vehicles (UUV). These compressed and encoded images were then further formed into acoustic packets. The objective was to transmit these acoustic packets, underwater, as representations of the sonar snippets (mugshots). The ability to transmit sonar snippets underwater while the UUV is underway is important as it allows the above-water operator to examine an image of mine-like objects, without recovering the UUV, for a timely decision on whether the object is actually a mine. This vector quantization method was used because of its terseness and thus it could be transmitted by WHOI underwater micromodems integrated on IVER3 UUVs. This presentation describes the algorithm, its implementation, and its initial in-water validation in local waters. This capability was also validated and demonstr...

Data analysis for image transmitted using Discrete Wavelet Transform and Vector Quantization compression

In this paper we are going to study the effect of channel noise in image compressed with vector quantization and discrete wavelet transform. The objective of this study is to analyze and understand the way that the noise attack transmitted data by doing lot of tests like dividing the indices in different levels according to discrete wavelet transform and dividing each level in frames of bits. The collected information well helps us to propose solutions to make the received image more resistible to the channel noise also to benefit from the good representation obtained by using vector quantization and discrete wavelet transform.

Very efficient variable-length codes for the lossless compression of VQ indices

In this paper, we propose a novel compression method that can efficiently compress a vector quantization (VQ) index table. Before compressing the VQ index table, the method sorts all of the codewords in the VQ codebook by principal component analysis (PCA), assuring that each codeword has extreme similarity to its adjacent codewords. Afterwards, in the VQ index table, the difference between the current compressed VQ index and one of its adjacent VQ indices is calculated as the compression code, and the indicators generated by the Huffman code method are used to identify the encoding length of each difference. In other words, each VQ index is replaced by one indicator and the difference, which are variable-length codes. The experimental results showed that the compression efficiency of the proposed method is superior to that of the other lossless data compression methods.

Subvector-Based Fast Encoding Method for Vector Quantization Without Using Two Partial Variances

Vector quantization (VQ) is a well-known method for signal compression. One of the main problems remaining unsolved satisfactorily in a VQ compression system is its encoding speed, which seriously constrains the practical applications of the VQ method. The reason is that in its encoding process VQ must perform many k-dimensional (k-D) expensive Euclidean distance computations so as to determine a best-matched codeword in the codebook for the input vector by finding the minimum Euclidean distance. Apparently, the most straightforward method in a VQ framework is to deal with a k-D vector as a whole vector. By using the popular statistical features of the sum and the variance of a k-D vector to estimate real Euclidean distance first, the IEENNS method has been proposed to reject most of the unlikely candidate codewords for a given input vector. Because both the sum and the variance are approximate descriptions for a vector and they are more precise when representing a shorter vector, it is better to construct the partial sums and the partial variances by dealing with a k-D vector as two lower dimensional subvectors to replace the sum and the variance of a whole vector. Then, by equally dividing a k-D vector in half to generate its two corresponding (k/2)-D subvectors and applying the IEENNS method again to each of the subvectors, the IEENNS method has been proposed recently. The SIEENNS method is so far the most search-efficient subvector-based encoding method for VQ, but it still has a large memory and computational redundancy. This paper aims at improving the state-of-the-art SIEENNS method by (1) introducing a new 3-level data structure to reduce the memory redundancy; (2) avoiding using two partial variances of the two (k=2)-D subvectors to reduce the computational redundancy, and (3) combining two partial sums of the two (k=2)-D subvectors together to enhance the capability of the codeword rejection test. Experimental results confirmed that the proposed method in this paper can reduce the total memory requirement for each k-D vector from (k + 6) to (k + 1) and meanwhile remarkably improve the overall search efficiency to 72.3–81.1% compared to the SIEENNS method.

Image steganography using vector quantization and discrete cosine transform

Application of the camouflaging technique achieves the goal of secure communication for secret images. The camouflaging technique in image, proposed by this article, combines lossy image compression technique vector quantization (VQ), lossless data compression and the cryptographic encryption/decryption procedure to decrease its deeply embedded secret data and increase its level of security. At last, the secret data are hidden in the frequency coefficients of the discrete cosine transform (DCT) domain of the cover image. There are two advantages of this proposed method: (a) The required secret data to be embedded by using VQ compression are less than the original and the time consumption to recover the secret image is more efficient, providing that the image quality is still visually satisfactory after restoration of the image, (b) If the stego-image had least-significant-bit malfunctioned, the secret image would still be distinguishable after restoring it from the damaged stego-image.