Fuzzy block truncation coding

The Lagrange multiplier optimization technique is used to derive an exact relationship between absolute moment block truncation coding (AMBTC) and the minimum mean square error quantizer (MMSEQ). In addition, using computer simulation it is shown that the perceptual difference between the decoded images using these two approaches is almost unnoticeable. Furthermore, AMBTC and MMSEQ are separately incorporated into a subband coding scheme. The perceptual difference between the reconstructed images with subband coding is unnoticeable. While the MMSEQ requires an iterated search for the optimum threshold AMBTC uses the local mean value as the threshold. As a result, AMBTC is more practical than the MMSEQ. >

Image Compression based on Block Truncation Coding Using Clifford Algebra

WITHDRAWN: Multi-classifiers and deep cnns for implementing image block truncation coding in e-learning platform

Objective: For reducing the data redundancy to save more hardware space and transmission bandwidth, data compression is used. Methods: For image compression, we have proposed a new method based on c-mean and absolute moment Block Truncation Coding (BTC) techniques. In this technique an image is segmented into several non-overlapping blocks. For each block, calculate the weighted c-mean by means of linear combination of its arithmetic mean and c-mean. The c-mean is evaluated by expressing every pixel into a sum of largest perfect square of non-negative integer. For each image block, the pixels are classified into two range of values based on weighted c-mean. The gray values of the image block which are greater than block weighted c-mean are considered as upper range and otherwise lower range. The weighted c-mean of upper range and lower range are termed as higher c-mean and lower c-mean respectively. Then construct the binary block matrix (0/1) based on weighted c-mean as a quantization level. In the decoder part, an image block is reconstructed by replacing the 1's with upper c-mean and 0's by lower c-mean of block matrix. Findings: The newly designed method is tested some8-bit standard images. It is also compared with related works in term of Peak Signal to Noise Ratio, Bit Rate, and Structural similarity index. It shows the effectiveness of the proposed method. Applications: Image storage is required for several purposes like document, medical images, magnetic resonance imaging and radiology, motion pictures etc. All such applications are based on image compression.

Data hiding encompasses a wide range of applications related to hiding messages in digital images. The visual redundancy of images makes it possible to embed data in the images without attracting attention. Increasing the hiding capacity and decreasing the hiding distortion are prime objectives that data hiding intends to achieve. In this paper, we propose a high-payload data hiding scheme based on absolute moment block truncation coding (AMBTC) to minimize the impact of hiding. A two-level minimum mean square error (MMSE) quantizer generated by AMBTC is used to decrease the distortion associated with hiding. Also, we present a lookup table based on the symmetric property for adaptively hiding secrets in pixels to achieve high hiding capacity. We can embed almost 1.9 bits per pixel (bpp) with a high image quality of an average of 31 dB. Only 5.3% of pixels are changed during the data-hiding process. Compared with other schemes, we can use 1 bpp more relative payload for embedding with the same stego image quality. The experimental results show that the proposed scheme has better hiding performance because it allows a huge amount of secret data to be hidden while maintaining the high visual quality of the stego image.

With images replacing textual and audio in most technologies, the volume of image data used in everyday life is very large. It is thus important to make the image file sizes smaller, both for storage and file transfer. Block Truncation Coding (BTC) is a lossy moment preserving quantization method for compressing digital gray level images. Even though this method retains the visual quality of the reconstructed image it shows some artifacts like staircase effect, etc. near the edges. A set of advanced BTC variants reported in literature were analyzed and it was found that though the compression efficiency is increased, the quality of the image has to be improved. An Improved Block Truncation Coding using k-means Quad Clustering (IBTC-KQ) is proposed in this paper to overcome the above mentioned drawbacks. A new approach of BTC to preserve the first order moments of homogeneous pixels in a block is presented. Each block of the input image is segmented into quad-clusters using k-means clustering algorithm so that homogeneous pixels are grouped into the same cluster. The block is then encoded by means of the pixel values in each cluster. Experimental analysis shows an improvement in the visual quality of the reconstructed image with high Peak Signal-to-Noise Ratio (PSNR) values compared to the conventional BTC and other modified BTC methods.

Digital image self embedding watermarking technology is one of the main technical means to solve the problem of image tamper detection and recovery. The existing self embedding watermarking technology has the problem of low authentication granularity. In this paper, an image self embedding semi fragile watermarking algorithm based on block truncation coding is proposed. Watermark generation and embedding: the image is divided into two levels at the block level, and the granularity is 4×4 and 2×2 image block; block truncation coding is used for each 4×4 image blocks are compressed and encoded to generate 2×2. Restore the watermark of the image block, and hash the restored watermark for the authentication watermark of the image block. Tamper authentication and recovery: a 4×4. Compare the watermark extracted from the image block with the watermark generated by the block to judge the two of the image block×whether the sub block two passes the authentication; restore the image block according to the authentication result.

Absolute Moment Block Truncation Coding (AMBTC) is an efficient and fast lossy compression technique for still images. It is easy to implement compared to transform coding. Interpolative AMBTC is a modified method of AMBTC compression. In this paper image data hiding in images using Interpolative AMBTC is proposed. In this method the secret image is stored in the bit plane of the AMBTC compressed image and is recovered during decompression process. Experimental results show that the proposed method achieves good quality decompressed image with low computational complexity.KeywordsData hidingCompressionBlock truncation codingAMBTCBit plane

In this paper, an optimized data embedding method based on Huang et al.’s work for absolute moment block truncation coding (AMBTC) is proposed. Huang et al.’s work successfully exploits the difference of quantization levels (QLs) for data embedment and has an excellent embedding performance. However, the modified QLs are not adjusted to minimize the distortion. In some rare cases, they might exceed the grayscale range. Moreover, the order of QLs in smooth blocks is not utilized for data embedment, losing the chance to embed one additional bit without deteriorating the image block. We propose a method to give analytical solutions to adjust QLs such that the distortions in both smooth and complex blocks are minimized. A subtle mechanism is also provided to ensure that no QLs will overflow or underflow. Moreover, the order of QLs is utilized in data embedment to further increase the payload without sacrificing the image quality. The experimental results reveal that the proposed method offers a better image quality over Huang et al.’s and other state-of-the-art works while providing a comparable or larger payload.

In this paper, an algorithm for binary codebook design has been used in vector quantization technique, which is used to improve the acceptability of the absolute moment block truncation coding (AMBTC) method. Vector quantization (VQ) method is used to compress the bitmap (the output proposed from the first method (AMBTC)). In this paper, the binary codebook can be engender for many images depending on randomly chosen to the code vectors from a set of binary images vectors, and this codebook is then used to compress all bitmaps of these images. The chosen of the bitmap of image in order to compress it by using this codebook based on the criterion of the average bitmap replacement error (ABPRE). This paper is suitable to reduce bit rates (increase compression ratios) with little reduction of performance (PSNR).

This paper describes a color image compression scheme based on absolute moment block truncation coding (AMBTC) using Huffman coding. High degree of correlation between the RGB planes of a color image is reduced by transforming them to Y, Cb, Cr planes. Each plane is then encoded separately using the proposed method. The experiment has been conducted using the proposed scheme on standard color images. Experimental results show that the compression ratio of the proposed method is significantly improved when compared to color BTC and color AMBTC, with a relative distortion of reconstructed images.

Lossless data hiding for color images based on block truncation coding

SummaryThis chapter proposes a novel color image compression technique based on Block Truncation Coding (BTC) using Cat Swarm Optimization (CSO). The original BTC method compresses a block of a grayscale image into a bitmap and a pair of quantization numbers. For a color image, which is encoded using R, G, and B channels, the BTC method enhances the compression rate using a common bitmap instead of three naturally formed bitmaps. However, it is difficult to find an efficient common bitmap due to its complexity. The CSO algorithm is generated by modeling the behaviors of cats, which show better performance in achieving the goal of finding best solution than other optimization algorithms. In this chapter, we adopt Cat Swarm Optimization (CSO) to search for a near optimal solution for this problem. The experimental results indicate that the proposed method could improve the decompressed BTC color image quality by obtaining a near optimal solution of the common bit map.

In this paper, we propose a simple reversible data hiding method in the spatial domain for block truncation coding (BTC) compressed grayscale images . The BTC method compresses a block of a grayscale image to a bitmap and a pair of quantization numbers. The proposed method first embeds secret bits into a block b y chang ing the order of those two quantization numbers. The compression rate is not enlarged by this embedding scheme. To further improve the hiding capacity, the proposed method embeds the secret bits into the bitmap of the block by exploiting the correlation characteristic of neighboring block s ’ bitmap in natural images. The experimental results show that the proposed method achieves very good hiding capacity at low compression rate.

The sheer size of volume data sampled in a regular grid requires efficient lossless and lossy compression algorithms that allow for on-the-fly decompression during rendering. While all hardware assisted approaches are based on fixed bit rate block truncation coding, they suffer from degradation in regions of high variation while wasting space in homogeneous areas. On the other hand, vector quantization approaches using texture hardware achieve an even distribution of error in the entire volume at the cost of storing overlapping blocks or bricks. However, these approaches suffer from severe blocking artifacts that need to be smoothed over during rendering. In contrast to existing approaches, we propose to build a lossy compression scheme on top of a state-of-the-art lossless compression approach built on non-overlapping bricks by combining it with straight forward vector quantization. Due to efficient caching and load balancing, the rendering performance of our approach improves with the compression rate and can achieve interactive to real-time frame rates even at full HD resolution.