Improved BTC Algorithm for Gray Scale Images Using K-Means Quad Clustering

Block truncation coding (BTC) is a well known lossy compression scheme. Due to its low complexity and easy implementation, BTC has gained wide interest in its further development and application for image compression. Based on simple thresholding, BTC retains sharp edges and thus leads to artifacts such as the staircase effect. The second problem encountered in BTC is the splitting of homogeneous regions, which produces false contours. In this work a fuzzy approach of BTC to avoid truncating homogeneous blocks and to preserve smooth edges in two-cluster blocks is proposed. Each image block, viewed as a fuzzy set, is segmented into two clusters using a fuzzy clustering algorithm. The block is then encoded by modified fuzzy weighted means of the two clusters. Initialization strategies of the fuzzy clustering algorithm and a contextual quantization method are proposed. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio when compared to BTC, economical BTC (EBTC), absolute moment BTC (AMBTC), and a minimum mean square error quantizer (MMSEQ). Computation time required by AMBTC, EBTC, and fuzzy BTC methods are reported.

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.

Block truncation coding (BTC) is a class of image compression algorithms whose main technique is the partitioning of an image into pixel blocks that are then each encoded using a representative set of pixel values. It is commonly used because of its simplicity and low computational complexity. The Quadtree-segmented BTC (QTS-BTC), which utilizes a dynamic hierarchical segmentation technique, is among the most efficient in the BTC class. In this study, we propose a new BTC variant that introduces two ideas: (1) the use of a k -d tree for segmentation and (2) the use of a Mean Squared Error (MSE) threshold for dynamically determining the granularity of the blocks. We refer to this new BTC variant as the k -d Tree Segmented BTC (KTS-BTC), and we test this against some of the existing BTC variants by running the algorithms on a standard image compression dataset. The results show that the proposed variant yields low bit rates of the compressed images, even outperforming the state-of-the-art QTS-BTC, without a significant reduction in image quality as measured using the Peak Signal-to-Noise Ratio (PSNR). The utilization of k -d tree for image segmentation is further shown to have more impact than that of employing the MSE thresholding scheme as a block activity classifier.

Image Compression based on Block Truncation Coding Using Clifford Algebra

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.

An image steganographic scheme is proposed in this article. The proposed scheme is implemented on the compression domain of image by applying block-truncation-coding (BTC) and histogram modification. The secret data are hidden into the BTC compression codes of image by exchanging the positions of the reconstructed values and by modifying the histogram generated from of the bitmap. Simulation results show that the performance is obviously improved by the proposed scheme in terms of the pure hiding capacity and the visual quality of stego-BTC-compressed image as compared with the earlier works. The averaged degradation of image quality, based on the peak signal to noise ratio (PSNR) criterion, is about 1.73 dB when it is compared with the original BTC-compressed images. Moreover, the proposed scheme is a reversible image steganographic scheme. The original BTC-compressed image can be losslessly reconstructed at the receiver after extracting the secret data. Therefore, we conclude that the proposed scheme is a feasible image steganographic scheme.

We describe a post-processing method which is designed to remove the picture impairments typically introduced by spatial domain block coding techniques, such as vector quantization (VQ) and block truncation coding (BTC). In these coding methods, the quantization noise is highly correlated with the characteristics of the original signals, so that different areas of the coded image suffer from distinctly different impairments. In particular, uniform areas suffer from the blocking effect, where the boundaries of adjacent blocks become visible resulting in false contours. Moreover, all diagonal edges appear jagged, which is known as the staircase effect. Finally, most of the textured areas are 'washed-out'. The post-processing method exploits the properties of smoothed image gradient data to distinguish between high and low detail regions of the decoded image. The filtering process is then decided based on the region classification. For the high detail image areas, which include edges and texture, a fully adaptive Gaussian-type filter is employed. The shape and the position of the Gaussian kernel is adapted according to the characteristics of each local region. For the low detail image regions, which include areas of constant or slowly varying intensity, a simple Gaussian smoothing operator is used. Simulation results confirm that, after applying this post-filtering algorithm to the coded images, the annoying blocking effect is reduced and the jagged edges appear continuous and sharp.

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.

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

A novel tamper detection scheme for BTC-compressed images

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.

Enhancing digital image integrity by exploiting JPEG bitstream attributes

This paper describes an efficient compression method for medical images and a high-speed display with a decoding function. An image is divided into blocks first and then whether each block has sharp edges is examined. The discrete-cosine-transform coding (DCT), which is efficient for a part having small gray-level variation, is applied to a block having no sharp edge. The block-truncation coding (BTC) which preserves sharp edges is applied to a block having edges, and then the DCT is applied to their error images. This combined procedure makes a reconstruction of the whole image in a high quality. The display developed by the authors receives the compressed data from a host computer and reconstructs images of good quality at high speed using four pipeline-based microprocessors based on their method. Experimental results show that the image quality of parts without sharp edges is as high as the DCT method; and that of parts with edges is better than the DCT and BTC methods. The whole process, which includes transferring data from the host computer to the display, reconstruction, and display of the data, took less than 5 s for a 1/10 compressed image. This demonstrates an excellent performance of the proposed reconstruction display.