VQ-compressed Images Research Articles

High Capacity Reversible Data Hiding for VQ-Compressed Images Based on Difference Transformation and Mapping Technique

In this paper, two reversible data hiding (RDH) methods for vector quantization (VQ)-compressed images are presented. In the first method, the difference-index table instead of the standard-index table is designed, and all difference indices are then classified into two groups, i.e., the to-be-mapped group and the candidate group, according to the occurrence frequencies of them. Afterwards, the mapping table between those two groups is adaptively constructed and used to serve for data embedding. Furthermore, another joint method is conducted by combining the standard-index based method and the difference-index based method, resulting in a better embedding performance. The experimental results confirm that both proposed methods, especially the joint method, are significantly superior to the most recent schemes in terms of the embedding capacity, bitrate and embedding rate.

An efficient histogram-based index mapping mechanism for reversible data hiding in VQ-compressed images

In this paper, a reversible data hiding scheme for vector quantization (VQ)-compressed images is proposed. The scheme introduces two methods to explore the high correlations between VQ indices of an index table, followed by exploiting the redundancy elimination for data embedding. The first method utilizes the concept of side matching and the second one uses the feature of locally repetitive occurrence of the VQ indices in VQ-compressed images. In this research, some concepts of information theory are studied to design an efficient index mapping mechanism in the embedding capacity point of view. The proposed index mapping mechanism partitions the positions of the sorted codebook into some intervals, and assigns each interval of the positions to each position with high hit rate in the sorted codebook. Then, based on the secret bits, each index located in the positions of the sorted codebook with high hit rates is mapped to one of the indices in the interval assigned to that position. Furthermore, an interval of the sorted codebook is reserved as the indicator of the indices with low hit rates. The experimental results demonstrate that the proposed scheme significantly outperforms the existing schemes in terms of the embedding capacity and compression-embedding efficiency.

A Lossless Data Hiding Scheme for VQ Indexes Based on Joint Neighboring Coding

Designing a new reversible data hiding technique with a high embedding rate and a low compression rate for vector quantization (VQ) compressed images is encouraged. This paper proposes a novel lossless data hiding scheme for VQ-compressed images based on the joint neighboring coding technique. The proposed method uses the difference values between a current VQ index and its left and upper neighboring VQ indexes to embed n secret bits into one VQ index, where n = 1, 2, 3, or 4. The experimental results show that the proposed scheme achieves the embedding rates of 1, 2, 3, and 4 bits per index (bpi) with the corresponding average compression rates of 0.420, 0.483, 0.545, and 0.608 bit per pixel (bpp) for a 256 sized codebook. These results confirm that our scheme performs better than other selected reversible data hiding schemes.

A novel legitimacy preserving data hiding scheme based on LAS compressed code of VQ index tables

Data hiding is a technique for secret and secure data storing and transmission that embeds data into a media such as an image, audio, video and so on, with minimal quality degradation of the media. Some developed data hiding schemes are reversible. Reversibility property allows the media to be recovered completely after extraction of the embedded data. Vector Quantization (VQ)-based image data hiding is one of the most popular study areas in the literature. However, most VQ-based reversible data hiding schemes generate non-legitimate codes as output. In other words output codes generated by such schemes could not be decoded by the conventional VQ or VQ based decoders and may arouse the attention of interceptors. On the other hand, the existing VQ based reversible data hiding schemes that generate legitimate VQ codes as output, suffer from low capacity and poor quality of stego-image. In this paper a novel reversible data hiding scheme for VQ-compressed images based on locally adaptive data compression scheme (LAS) is proposed. Unlike other schemes, the proposed scheme doesn't change the VQ indices; data is embedded by choosing one of the possible ways to encode each index. As a result, in comparison with the schemes that embed data by index replacement, in the proposed scheme no extra distortion is made by data embedding and the outputted codes are compatible with the conventional LAS decoder. These properties help to hide the existence of secret data and make the scheme suitable for steganography. Moreover, a framework to combine the proposed scheme with some other schemes to improve their capacity and embedding side information is proposed. Since LAS is a general data compression scheme, the proposed scheme could be used to embed data into any data formats. All existing LAS based data hiding schemes produce non-legitimate codes as their outputs and the proposed scheme is the first and only one that produces legitimate codes as output. Experimental results show that the proposed scheme outperforms the existing LAS based schemes and some other VQ based data hiding schemes. On average, the proposed scheme embeds 2.14 bits per index with almost the same bit-rate as the bit-rate of the VQ index table.

Reversible data hiding for VQ-compressed images based on search-order coding and state-codebook mapping

In the field of digital multimedia, numerous researchers have exploited data hiding for providing secure communications. Because most multimedia is compressed in advance to save storage space and transmission time, this paper presents a reversible data-hiding scheme for vector quantization (VQ)-compressed images. In the proposed scheme, search-order coding (SOC) is first employed to reduce the size of the VQ index table. For image blocks that cannot be processed by SOC, a technique called state-codebook mapping (SCM) is used to map the blocks to the corresponding indices in their state-codebooks, which are generated by a set of neighborhood indices. After SOC and SCM, the size of the output code stream is reduced, and a large amount of space is available for embedding sensitive data. Performance comparisons with similar recent studies are presented for demonstrating the superiority of the proposed scheme. The experimental results indicated that the proposed scheme can achieve a high embedding capacity and a high compression bit rate.

Information Hiding Based on Block Match Coding for Vector Quantization-Compressed Images

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.

A low distortion reversible data hiding scheme for search order coding of VQ indices

In recent years many reversible data hiding schemes for vector quantization (VQ)-compressed images have been developed. However most of them generate non-legitimate codes as output and make the data hiding behavior detectable. Moreover existing schemes with legitimate outputs usually need side information to achieve reversibility. In this paper a reversible data hiding scheme for VQ-compressed images based on search order coding (SOC) is proposed which generate legitimate SOC codes as output. For each index of the input VQ index table which finds a match in its corresponding SOC path, the indices located in the SOC path are clustered, and then SOC codes of some blocks are changed to SOC code of one of the indices in the same cluster to hide data. Some SOC codes are re-encoded using original index value (OIV) codes for recovery purpose. By the strategy of re-encoding, the proposed scheme doesn't need any side information. The proposed scheme adaptively adjusts the criteria of exchangeability of the indices based on the smoothness level of each image block to prevent appearing spots in the stego-image. Experimental results show that the proposed scheme on average could embed about 0.5 bits per index by only about 0.67 dB degradation of image quality for a set of test images includes smooth an complicated images. Also superiority of the proposed scheme when compared with the previous schemes is confirmed from various aspects; hiding capacity, stego-image quality, the average of the bit rate of the output code, and the execution time.

Efficient reversible data hiding for VQ-compressed images based on index mapping mechanism

In this paper, we propose a novel reversible data-hiding scheme in the index tables of the vector quantization (VQ) compressed images based on index mapping mechanism. On the sender side, the VQ indices with zero occurrence numbers in a given index table of an image are utilized to construct a series of index mappings together with some indices with the largest occurrence numbers. The indices in each constructed mapping correspond to the full binary representations with the length of the mapping bit number. Through the mapping optimization by index histogram, the optimal vector of mapping bit numbers can be obtained, which leads to the highest hiding capacity. Data embedding procedure can be easily achieved by the simple index substitutions according the current subset of secret bits for hiding. The same index mappings reconstructed on the receiver side ensure the correctness of secret data extraction and the lossless recovery of index table. Experimental results demonstrate the effectiveness of the proposed scheme.

Lossless data hiding for VQ indices based on neighboring correlation

Data hiding is one of the protective techniques for authentication or secret communication through a public and open channel such as the Internet. In this study, a novel lossless data hiding by embedding secret data into a Vector Quantization (VQ)-compressed image is proposed to achieve secret communication and data compression simultaneously. The correlation of neighboring blocks of a VQ-compressed image is explored. It is shown that the neighboring blocks of a VQ-compressed image normally have high mutual correlation. Thus, this scheme employs the neighboring processed compression indices to generate specific sub-codebooks required for encoding and hiding data simultaneously. Since the sizes of sub-codebooks are smaller than that of the original VQ codebook, the encoded size of each index can be significantly reduced. As a result, a great deal of extra free space can be created. Moreover, the original VQ-compressed images can be perfectly recovered after data extraction. 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 the former schemes in terms of compression ratio, embedding rate, execution time, and embedding capacity.

Reversible Data Hiding Based on Search-order Coding for VQ-compressed Images

Reversible Data Hiding Based on Search-order Coding for VQ-compressed Images

Reversible steganography based on side match and hit pattern for VQ-compressed images

Data hiding is an important technique in multimedia security. Multimedia data hiding techniques enable message senders to disguise secret data by embedding them into cover media. Thus, delivering secret messages is as easy as sending the cover media. Recently, many researchers have studied reversible data hiding for images. Those methods can reconstruct the original cover image and extract the embedded secret data from a stego-image. This study proposes a novel reversible steganographic method of embedding secret data into a vector quantization (VQ) compressed image by applying the concept of side match. The proposed method uses extra information, namely the hit pattern, to achieve reversibility. Moreover, its small hit pattern enables the embedding of the entire hit pattern along with the secret data in most cases. To optimize visual quality of the output stego-image, the method applies the concept of partitioned codebooks (state codebooks). The partition operation on the codebook uses a look-up table to minimize embedding and extraction time. We also propose the use of diagonal seed blocks to embed the entire hit pattern into the cover image without producing any extra control messages. Compared to the Chang and Lin method, the experimental results show that the proposed method has higher capacity, better visual quality, and shorter execution time.

A novel data hiding and reversible scheme using SMN blocks in VQ-compressed images

AbstractData hiding is a technique for embedding secret data into cover media. It is important to multimedia security and has been widely studied. Reversible data hiding methods are becoming prevalent in the area because they can reconstruct the original cover image while extracting the embedded data. In this paper, we propose a new reversible method for vector quantization (VQ) compressed images. Our method takes advantages of the relationship among the side match neighbouring (SMN) blocks to achieve reversibility. The experimental results show that the proposed method has higher compression rate and larger capacity than other existing reversible methods.

Fractal curves to improve the reversible data embedding for VQ-indexes based on locally adaptive coding

The VQ reversible data embedding technology allows an original VQ coding to be completely restored after the extraction of embedded data. In this paper, we propose a new reversible scheme based on locally adaptive coding for VQ-compressed images. The fractal Hilbert curve is applied to replace the traditional trace of processing the VQ index table. The VQ index table is pre-processed to create a fractal Hilbert curve. Following the curve to process the VQ index table can get better compression rates in the data embedding procedure. Besides, compared to Chang et al.’s scheme, which compressed the inputted VQ index value only when the to-be-embedded bit b is 0, our method performs compressing operations in both cases that the to-be-embedded bits b are 0 and 1. The experimental results show that the proposed method has the best compression rate and the highest embedding capacity compared with other reversible VQ embedding methods.

An adaptive reversible information hiding method based on search-order coding for VQ-compressed images

In the last decade, a common information hiding technique applied to lossy compressed images embeds secret data by adjusting the indices formed by the vector quantization (VQ) scheme. After extracting embedded data, this method cannot recover the original VQ-compressed codes. This method is called the irreversible information hiding method. On the contrary, the technique which is able to recover the original VQ-compressed codes after extracting of the embedded data is called the reversible information hiding method. There is little research related to reversible information hiding methods for the VQ-compressed images. In this paper, we propose a simple and adaptive reversible information hiding method applied to a VQ-compressed image. In our method, the VQ-compressed image is further compressed using the search-order coding algorithm. It simultaneously embeds the data bits into the indices. Our method embeds any length of data bits into the VQ-compressed image. The experiment results show our method outperforms other methods in embedding capacity and compression ratio.

Adaptive embedding techniques for VQ-compressed images

An embedding algorithm, which can adaptively embed a binary message into a VQ-compressed image, is proposed in this paper. The proposed algorithm is divided into three phases. In the codeword grouping phase, a new group of codewords is initiated by the two most similar codewords which do not belong to any group. For each codeword which does not belong to any group, if the codeword is similar to all of the codewords in the group, it will be added to the group. In the embedding phase, each codeword in a group will be assigned to embed a certain sub-message whose length is determined by the number of codewords in the group. The more codewords a group has, the higher the embedding capacity of a codeword in the group will be. In the extracting phase, given a codeword and the number of codewords in the group to which the codeword belongs, the embedded message can be extracted from the codeword by simply determining the order of the codeword in the group. Experimental results show that the proposed algorithm performs better than previous algorithms with regard to embedding capacity and image quality. For the test images, when the embedding capacity is less than 5 bits per codeword index, the difference of the PSNR values between the stego-image and its VQ-compressed cover image will be no more than 5 dB on average.

Reversible Data Hiding in the VQ-Compressed Domain

Steganographic methods usually produce distortions in cover images due to the process of embedding secret bits. These distortions are hard to remove, and thus the cover image cannot be recovered. Although the distortions are always small, they cannot be allowed for some sensitive applications. In this paper, we propose a reversible embedding scheme for VQ-compressed images, which allows the original cover image to be completely recovered after the extraction of the secret bits. The embedded payload in the proposed method comprises the secret bits plus the restoration information. In order to reduce the size of payload, we utilized the spatial correlations in the image as the restoration information and then compressed the correlations by a lossless compression method. In addition, an alternative pairing method for codewords was proposed to improve the stegoed image quality and control the embedding capacity. Experimental results showed that the proposed method has the benefit of high efficiency of the steganographic process, high image quality, and adaptive embedding capacity compared with other schemes.

Anti-Pseudo-Gray Coding for VQ Encoded Images over Noisy Channels

In this paper, an improved VQ index reassignment algorithm is proposed to reduce the block degradation of VQ encoded images caused by noisy channels. By reassigning the indices of a VQ codebook, the VQ-compressed images can be transmitted through a noisy channel while preserving the quality of reconstructed VQ images. Pseudo-Gray coding (PGC) and anti-Gray coding (AGC) are two famous index reassignment schemes. By combining the virtues of PGC and AGC skillfully, a novel VQ reassignment algorithm is proposed. Experimental results show that the proposed algorithm outperforms AGC and PGC. Additionally, the computational complexity of index reassignment in the proposed algorithm is reduced significantly

Reversible Steganography for VQ-Compressed Images Using Side Matching and Relocation

The reversible steganographic method allows an original image to be completely reconstructed from the stegoimage after the extraction of the embedded data. The traditional reversible embedding schemes are not suitable for images compressed using vector quantization (VQ) and usually require the use of the location map for reversibility. In this paper, we propose a reversible embedding scheme for VQ-compressed images that is based on side matching and relocation. The new method achieves reversibility without using the location map. The experimental results show that the proposed method is practical for VQ-compressed images and provides high image quality and embedding capacity

Reversible steganographic method using SMVQ approach based on declustering

Reversible steganography allows an original image to be completely restored after the extraction of hidden data embedded in a cover image. In this paper, we propose a reversible scheme for VQ-compressed images that is based on a declustering strategy and takes advantage of the local spatial characteristics of the image. The main advantages of our method are ease of implementation, low computational demands, and no requirement for auxiliary data. The experimental results show that the embedding capacity of the proposed method is controlled by the number of declustered groups and the spatial characteristics of the cover image. The results also show that the proposed method has the best embedding capacity and the least running time compared with other methods.

Regularized restoration of vector quantisation compressed images

The authors study the application of image restoration technology in improving the coding performance of a vector quantisation (VQ) image compression codec. Restoration of VQ-compressed images is rarely addressed in the literature, and direct applications of existing restoration techniques are generally inadequate to deal with this problem. A restoration algorithm is proposed, specific to VQ-compressed images, that makes good use of the codebook to derive useful a priori information for restoration. The proposed restoration algorithm is shown to be capable of improving the quality of a VQ-compressed image to a much greater extent, compared with other existing restoration approaches. As no extra information, other than the codebook, is required to carry out the restoration with the proposed algorithm, no transmission overhead is necessary and, hence, it can be fully compatible with any VQ codec when used to improve coding performance.