Block Truncation Research Articles

Improved color image coding schemes based on single bit map block truncation coding

Two color image coding schemes based on single bit map block truncation coding are proposed in this paper. The first scheme employs the optimal rule for single bit map generation. In addition, the quantization level recomputation process is designed. By using these two techniques, a fixed bit rate image coding scheme is introduced. To further cut down the required bit rates of the first scheme, the similar block prediction technique and the bit map omission technique are employed in the second scheme. According to the experimental results, the first scheme proposed significantly improves the image qualities of the compressed images compared to the traditional single bit map block truncation coding scheme. In addition, good image qualities of the compressed images are achieved by the second scheme while keeping bit rates low.

A novel fractal coding method based on M-J sets.

In this paper, we present a novel fractal coding method with the block classification scheme based on a shared domain block pool. In our method, the domain block pool is called dictionary and is constructed from fractal Julia sets. The image is encoded by searching the best matching domain block with the same BTC (Block Truncation Coding) value in the dictionary. The experimental results show that the scheme is competent both in encoding speed and in reconstruction quality. Particularly for large images, the proposed method can avoid excessive growth of the computational complexity compared with the traditional fractal coding algorithm.

COLOR IMAGE COMPRESSION BASED ON ABSOLUTE MOMENT BLOCK TRUNCATION CODING USING DELTA ENCODING AND HUFFMAN CODING

Abstract A simple and easy to implement technique for improving absolute moment block truncation coding (AMBTC) is proposed. AMBTC method produce a fixed length binary representation of each block according to the block size of the image. In contrast huffman coding produce a variable length binary representation based on the statistical nature of the image. So the propsed scheme presented to improve the performance of AMBTC to achieve variable length compression by applying the combination of delta encoding , and huffman coding. The Performance of the proposed scheme is compared with the original BTC , and AMBTC in terms of compression ratio (Cr), root mean square erorr (RMSE), and peak signal-to-noise ratio (PSNR).Simulation results indicate that the compression ratio of the proposed algorithm is much higher than that of the original BTC , and AMBTC with a relative distortion of image quality in the reconstructed images. Keywords: Image compression; Absolute moment block truncation coding; Delta encoding ;Huffman coding.

A Tamper-Detection Scheme for BTC-Compressed Images with High-Quality Images

This paper proposes a novel image authentication scheme, aiming at tampering detection for block truncation coding (BTC) compressed image. The authentication code is generated by using the random number generator with a seed, and the size of the authentication code is based on the user’s requirement, with each BTC-compressed image block being used to carry the authentication code using the data hiding method. In the proposed scheme, to obtain a high-quality embedded image, a reference table is used when the authentication code is embedded. The experimental results demonstrate that the proposed scheme achieves high-quality embedded images and guarantees the capability of tamper detection.

Image Compression based on 4 Level AMBTC

Image compression is essential for different applications such as transmission and storage in data bases. Two methods use for image compression- lossy and lossless. In this paper we discuss about the image compression and propose a new method of lossy image compression. Its principle is based on absolute moment block truncation coding (AMBTC). In BTC, there is different method that is improving the PSNR and reduces the MSE. Keywordscompression, BTC, AMBTC, PSNR, MSE

A Novel Non-Uniform BTC and its Application

In this paper, a novel image representation coding based on non-uniform Block Truncation Coding (BTC) is proposed. A given image can automatically be partitioned into different regions with different sizes and the bivariate piecewise polynomials are used to do the Least Square Approximation for the pixel values in each sub-region based on BTC bitmap. When the approximation error and initial partition are specified, a specific image partition result is obtained. The image re-construction quality of the proposed algorithm is better than that of the traditional BTC. Based on this algorithm, an effective denoising scheme of image is implemented and some of the experimental examples are illustrated to prove that the quality of the re-constructed image, the denoising effect are all satisfactory and can be referenced by other researchers.

High payload image steganography with minimum distortion based on absolute moment block truncation coding

This paper presents an improved image steganography scheme based on absolute moment block truncation coding (AMBTC). The purpose of the proposed scheme is to achieve high payload, good visual quality and low computation complexity at the same time. In the scheme, a threshold is predefined to classify the blocks of the AMBTC-compressed codes as smooth or complex blocks, in which data are then embedded. For the smooth blocks, the bit planes of them are used to embed the data. Later, the two quantization levels in the smooth block are re-calculated to minimize the distortion in image quality. For the complex blocks, a proportion of secret bits are concealed by exchanging the order of two quantization levels with together toggling the bit plane, by which the payload can be increased without any distortion. Furthermore, the proposed scheme inherits the advantages of the AMBTC method, such as pleasing image quality, ease to be implemented and low computational complexity. With adjustable threshold, the application of the proposed scheme becomes flexible, that means different thresholds can be used for different applications. Experimental results and analysis demonstrate the effectiveness and superiority of the proposed scheme.

The method for image retrieval based on multi-factors correlation utilizing block truncation coding

In this paper, we proposed multi-factors correlation (MFC) to describe the image, structure element correlation (SEC), gradient value correlation (GVC) and gradient direction correlation (GDC). At first, the RGB color space image is converted to a bitmap image and a mean color component image utilizing the block truncation coding (BTC). Then, three correlations will be used to extract the image feature. The structure elements can effectively represent the bitmap which is generated by BTC, and SEC can effectively denote the bitmap׳s structure and the correlation of the block in the bitmap. GVC and GDC can effectively denote the gradient relation, which is computed by a mean color component image. Formed by SEC, GVC and GDC, the image feature vectors can effectively represent the image. In the end, the results demonstrate that the method has better performance than other image retrieval methods in the experiment.

A Semi-fragile Image Watermarking Scheme Exploiting BTC Quantization Data

This paper proposes a novel blind image watermarking scheme exploiting Block Truncation Coding (BTC). Most of existing BTC-based watermarking or data hiding methods embed information in BTC compressed images by modifying the BTC encoding stage or BTC-compressed data, resulting in watermarked images with bad quality. Other than existing BTC-based watermarking schemes, our scheme does not really perform the BTC compression on images during the embedding process but uses the parity of BTC quantization data to guide the watermark embedding and extraction processes. In our scheme, we use a binary image as the original watermark. During the embedding process, the original cover image is first partitioned into non-overlapping 4×4 blocks. Then, BTC is performed on each block to obtain its BTC quantized high mean and low mean. According to the parity of high mean and the parity of low mean, two watermark bits are embedded in each block by modifying the pixel values in the block to make sure that the parity of high mean and the parity of low mean in the modified block are equal to the two watermark bits. During the extraction process, BTC is first performed on each block to obtain its high mean and low mean. By checking the parity of high mean and the parity of low mean, we can extract the two watermark bits in each block. The experimental results show that the proposed watermarking method is fragile to most image processing operations and various kinds of attacks while preserving the invisibility very well, thus the proposed scheme can be used for image authentication.

EMD를 사용한 BTC 기반의 데이터 은닉

Block truncation coding (BTC) is a recent technique used for compression of monochrome image data. The original algorithm of BTC implement to use the standard mean and the standard deviation. For data hiding, it is needed to modify bit-planes of an images. These modifications yields unacceptable quality of BTC images. Thus, we propose IODBTC (Improved Ordered dithering BTC) improving BTC. In this paper, we improve this problem. Experimental results show that the reconstructed secret messages are the same as the original secret messages, and that the proposed scheme exhibits a good BTC images compared to that of previous schemes.

Reversible Data Hiding Scheme for BTC-compressed Images Based on Histogram Shifting

In this paper, we proposed a novel reversible data hiding scheme for the compressed images for block truncation coding (BTC). In this scheme, the secret data is embedded into the compressed codes of BTC. The histogram shifting technique is employed in the proposed scheme to embed the secret data into the quantization levels of the compressed codes. After the data embedding procedure is executed, the embedded compressed codes still follow the standard format of BTC. The experimental results reveal that the proposed scheme provides good image quality of the embedded image.

Variable-Rate Quadtree-segmented Block Truncation Coding for Color Image Compression

A novel color image compression scheme based on block truncation coding is proposed in this paper. In this scheme, the quadtree segmentation technique is employed to partition the color image into variable-sized image blocks according to its block activities. Then, the block truncation coding scheme with the bit map omission technique is employed to encode the image blocks. According to the experimental results, the proposed scheme significantly cuts down the bit rates while keeping good reconstructed image qualities of the compressed color images.

Reversible AMBTC-based secret sharing scheme with abilities of two decryptions

This paper presents a secret sharing scheme to encode a secret binary image in images which are compressed by absolute moment block truncation coding (AMBTC) method. The generated shadows by the proposed scheme are in AMBTC-compressed format and meaningful, the visual quality of which is the same as that of its original AMBTC version. In the proposed scheme, the secret image can be reconstructed in two situations: (1) if some light-weight computational devices are available, the secret image can be completely decrypted without any distortion, and (2) otherwise, the secret image can also be revealed by human visual system. Furthermore, the proposed scheme provides the reversibility for protecting the valuable cover images. In addition, underlying the proposed (2,2) scheme, this paper gives a construction for the (2,n) scheme where one participant may require more than one shadow. Theoretical analysis and simulation results demonstrate the superiority of the proposed scheme.

A Comparison of Lossless Steganography Using I-AMBTC Technique for Various Block Sizes

Steganography is the art of hiding a message in an image and also the fact that there is a message inside the image. Steganography is a type of information hiding technique which embeds a secret message in a image,vedio,audio etc called the cover file. The main purpose of steganography is to provide security to personal or public data. In this paper Interpolative Absolute Moment Block Truncation Coding(I-AMBTC) is used for compressing the cover image and to embed the secret data into the cover. I-AMBTC compression is more than the AMBTC technique as only half of the number of pixels in the binary converted image are transmitted. Here a comparison of the I-AMBTC technique is done for 2x2, 4x4, 8x8 block sizes. I-AMBTC is a lossless technique as the cover image and the secret image can be recovered completely. Index Terms: I-AMBTC, AMBTC, Information hiding , Steganography, Cryptography

A novel reversible data hiding scheme based on AMBTC compression technique

Data hiding encompasses a wide range of applications for embedding messages in content. However, hiding data inevitably destroys the host image, even though the distortion is imperceptible. To enhance the hiding capacity and maintain the quality of the host image after embedding hidden data, in this paper, we present a high payload reversible data hiding scheme that is based on the absolute moment block truncation coding (AMBTC) compression domain. We explore the redundancy in a block of AMBTC-compressed images to determine if a block is embeddable or non-embeddable. Next, we create four disjoint sets for embeddable blocks to embed data using different combinations of the mean value and the standard deviation. Performance comparisons with other BTC-based schemes are provided to demonstrate the superiority of the proposed scheme.

Novel Algorithm for RS Image Classification

In the present era of modern technology, the efficacy and accuracy of output is demanding. Based on rigorous survey, Bharatkar and Patel (International Journal of Advanced Research in Computer Science 3(7):218–223, 2012) concluded that the incorporation of Block Truncation Coding (BTC) approach in the existing image classification algorithm can be used to improve the classification accuracy. Therefore, in the present study, an effort has been made to explore the Content Based Remote Sensing Image (CBRSI) classification algorithm to enhance classification accuracy with BTC approach. It is revealed from the study that BTC based maximum likelihood classifier gives better overall accuracy and kappa statistics.

A reversible steganographic algorithm for BTC-compressed images based on difference expansion and median edge detector

In this paper, we propose a reversible steganographic algorithm for compressed images. The algorithm firstly compresses the input image using block truncation coding. One binary map and two quantisation levels, called high and low levels, are then obtained for each block. Thereafter, we adopt a median edge detector to predict the high and low quantisation levels for neighbouring blocks. A secret message is then embedded into the predicted difference based on the difference expansion technique. Each block can be classified as embeddable and non-embeddable according to the order of two quantisation levels. Thus, the location map is unnecessary in our proposed algorithm. The experimental results show that our data-embedded compressed code can be the same file size compared with standard block truncation coding-compressed code. Our algorithm can also resist the RS steganalysis attack. Further, the embedding capacity can be varied according to the given embedding parameter. The feasibility of our proposed algorithm is validated by presenting comparisons with existing algorithms.

Image indexing using the color and bit pattern feature fusion

This paper presents a new way to index a color image by exploiting the low complexity of the Ordered-Dither Block Truncation Coding (ODBTC) for generating the image features. Image content descriptor is directly constructed from two ODBTC quantizers and the corresponding bitmap image without performing the decoding process. The color co-occurrence feature (CCF) derived from the ODBTC quantizers captures the color distribution and image contrast in block based manner, while the Bit Pattern Feature (BPF) characterizes image edges and visual patterns. The similarity between two images can be easily determined based on their CCF and BPF under a specific distance metric measurement. A metaheuristic algorithm, namely Particle Swarm Optimization (PSO), is employed to find the optimum similarity constants and improve the retrieval accuracy. Experimental results demonstrate that the proposed indexing method is superior to the former Block Truncation Coding (BTC) image retrieval system and the other existing methods. The ODBTC method offers an effective way to index an image in a content-based image retrieval system, and simultaneously it is able to compress an image efficiently. Thus, this system can be a very competitive candidate in image retrieval applications.

Wavelet based dynamic Mel Frequency Cepstral Coefficients (MFCC) and block truncation techniques for efficient speaker identification under narrowband noise conditions

Speaker identification strategies are well convincing in their performance when clean speeches are scrutinized. But the performance degrades when speech samples are corrupted by narrowband noise. Block truncation of the cepstral coefficients ensures that not all the features are affected by narrowband noise but it cannot reduce the extent of degradation. This work is focused towards improving the performance of speaker identification systems by block truncating the features which are subjected to wavelet processing. Wavelet decomposition divides the entire energy spectrum of the speech signal into bands corresponding to the number of levels of decomposition performed in the wavelet transformation thereby segregating the noise affected bands from other bands. In addition to that, wavelet filters provide the smoothening of the noisy speech signals which enhances the identification of the correct speaker. Dynamic Mel filtering of these wavelet coefficients followed by block truncation provides better identification, taking advantage of the fact that some filter bank coefficients remain unaffected by narrowband noise. The features are modeled by Gaussian mixture model - Universal background model (GMM-UBM) that serves as a generic one timed trained model. Speaker identification efficiency of 97.23% is achieved through this wavelet based dynamic MFCC technique which exhibits 7.58% improvement in speaker identification accuracy when compared with non wavelet based block truncation method. Key words: Wavelet decomposition, block truncation, Dynamic Mel Filtering Cepstral Coefficients (DMFCC), Gaussian mixture model - Universal background model (GMM-UBM), speaker identification.

A secure removable visible watermarking for BTC compressed images

A novel removable visible watermarking (RVW) algorithm by combining Block Truncation Coding (BTC) and chaotic map (RVWBCM) is presented in this paper. It embeds a visible watermark in the BTC codes...