Block Truncation Coding Algorithm Research Articles

A Review on Image Compression Using Halftoning Based BTC

In this paper scrutinizes image compression using Halftoning Based Block Truncation Coding for color image. Many algorithms were selected likely the original Block Truncation coding, Ordered Dither Block Truncation Coding, Error Diffusion Block Truncation Coding , and Dot Diffused Block Truncation Coding . These above techniques are divided image into non overlapping blocks. BTC acts as the basic compression technique but it exhibits two disadvantages such as the false contour and blocking effect. Hence halftoning based block truncation coding (HBTC) is used to overcome the two issues. Objective measures are used to evaluate the image degree of excellence such as Peak Signal to Noise Ratio, Mean Square Error, Structural Similarity Index and Compression Ratio. At the end, conclusions have shown that the Dot Diffused Block Truncation Coding algorithm outperforms the Block Truncation Coding as well as Error Diffusion Block Truncation Coding.

A Common Bitmap Block Truncation Coding for Color Images Based on Binary Ant Colony Optimization

For the compression of color images, a common bitmap usually is generated to replace the three individual bitmaps that originate from block truncation coding (BTC) of the R, G and B channels. However, common bitmaps generated by some traditional schemes are not the best possible because they do not consider the minimized distortion of the entire color image. In this paper, we propose a near-optimized common bitmap scheme for BTC using Binary Ant Colony Optimization (BACO), producing a BACO-BTC scheme. First, the color image is compressed by the BTC algorithm to get three individual bitmaps, and three pairs of quantization values for the R, G, and B channels. Second, a near-optimized common bitmap is generated with minimized distortion of the entire color image based on the idea of BACO. Finally, the color image is reconstructed easily by the corresponding quantization values according to the common bitmap. The experimental results confirmed that reconstructed image of the proposed scheme has better visual quality and less computational complexity than the referenced schemes.

A Comparative Study on Image Compression Using Half toning Based Block Truncation Coding for Color Image

In this paper scrutinizes image compression using Halftoning Based Block Truncation Coding for color image. Many algorithms were selected likely the original Block Truncation coding, Ordered Dither Block Truncation Coding, Error Diffusion Block Truncation Coding , and Dot Diffused Block Truncation Coding. These above techniques are divided image into non overlapping blocks. BTC acts as the basic compression technique but it exhibits two disadvantages such as the false contour and blocking effect. Hence halftoning based block truncation coding (HBTC) is used to overcome the two issues. Objective measures are used to evaluate the image degree of excellence such as Peak Signal to Noise Ratio, Mean Square Error, Structural Similarity Index and Compression Ratio. At the end, conclusions have shown that the Dot Diffused Block Truncation Coding algorithm outperforms the Block Truncation Coding as well as Error Diffusion Block Truncation Coding.

A Block Truncation Coding Algorithm and Hardware Implementation Targeting 1/12 Compression for LCD Overdrive

Block truncation coding (BTC) is commonly used to compress a video to be stored in frame memory for display devices such as LCDs. The original BTC algorithm partitions an input image into 4 $\times$ 4 blocks and compresses each block to reduce the data size down to 1/4 of the original size of the data. As the size of a video displayed on an LCD increases, the frame memory size also increases. Therefore, it is necessary to reduce the frame memory size further. The previous BTC suffers from a severe quality degradation when its compression ratio exceeds 6 to the original data. This paper proposes a novel BTC-based compression algorithm of which the target compression ratio is 12. To improve the compression efficiency, the proposed algorithm adopts a bit-saving scheme that utilizes the spatial correlation between vertically adjacent blocks. Furthermore, the blocks with low image complexity are coded using one 2 $\times$ 16 coding block while those with high image complexity are coded using two 2 $\times$ 8 coding blocks. With the hardware implementation for a high throughput constraint, the memory sizes of the encoder and decoder are 21,312 bits and 5952 bits, respectively, whereas the gate counts of the encoder and decoder are 68.7 K and 13.6 K, respectively. Experimental results show that the average PSNR of the proposed algorithm is 30.03 dB and that the throughputs of the encoder and decoder are 27.5 Gbps and 63.9 Gbps at operating frequencies of 143 and 333 MHz, respectively.

Modified BTC Algorithm for Audio Signal Coding

This paper describes modification of a well-known image coding algorithm, named Block Truncation Coding (BTC) and its application in audio signal coding. BTC algorithm was originally d ...

An Adaptive Block Truncation Coding Algorithm Based on Data Hiding

An Adaptive Block Truncation Coding Algorithm Based on Data Hiding

Ratio‐modified block truncation coding algorithms for reduced bitrates

Block truncation coding (BTC) technique is a simple and fast image compression algorithm since complicated transforms are not used. The principle used in BTC algorithm is to use two‐level quantiser that adapts to local properties of the image while preserving the first‐ or first‐ and second‐order statistical moments. The parameters transmitter or stored in the BTC algorithm are statistical moments and bitplane yielding good quality images at a bitrate of 2 bits per pixel (bpp). In this paper, two algorithms for modified BTC (MBTC) are proposed for reducing the bitrate below 2 bpp. The principal used in the proposed algorithms is to use the ratio of moments which is a smaller value when compared to absolute moments. The ratio values are then entropy coded. The bitplane is also coded to remove the correlation among the bits. The proposed algorithms are compared with MBTC and the algorithms obtained by combining JPEG standard with MBTC in terms of bitrate, peak signal‐to‐noise ratio (PSNR) and subjective quality. It is found that the reconstructed images obtained using the proposed algorithms yield better results.

Improved Vector Quantization Based Block Truncation Coding Using Template Matching and Lloyd Quantization

Block truncation coding (BTC) is an efficient image compression algorithm that generates a constant output bit-rate. For color image compression, vector quantization (VQ) is exploited to improve the coding efficiency. In this letter, we propose an improved VQ based BTC (VQ-BTC) algorithm using template matching and Lloyd quantization (LQ). The experimental results show that the proposed method improves the PSNR by 0.9dB in average compared to the conventional VQ-BTC algorithms.

Improved Block Truncation Coding Based on the Void-and-Cluster Dithering Approach

Block Truncation Coding (BTC) is an efficient technology for image compression. An improved BTC algorithm, namely Ordered Dither Block Truncation Coding (ODBTC), is presented in this study. In order to provide better image quality, the void-and-cluster halftoning is combined with the BTC. The ODBTC results show that the image quality is improved when it is operated in high coding gain applications. Another feature of the ODBTC is the dither array Look Up Table (LUT), which significantly reduces the complexity compared to the BTC.

An FPGA implementation of a neural optimization of block truncation coding for image/video compression

This paper presents a Field Programmable Gate Array (FPGA) implementation for image/video compression using an improved block truncation coding (BTC) image compression technique. The improvement is achieved by employing a Hopfield neural network (HNN) to calculate a cost function upon which a block is classified as either a high- or a low-detail block. Accordingly, different blocks are coded with different bit rates and thus resulting in better compression ratios. The paper formulates the utilization of HNN within the BTC algorithm in such a way that a viable FPGA implementation is produced. The implementation exploits the inherent parallelism of the BTC/HNN algorithm to provide efficient algorithm-to-architecture mapping. The Xilinx VirtexE BTC implementation has shown to provide a processing speed of about 1.113x10^6 of pixels per second with a compression ratio which varies between 1.25 and 2bits/pixel, according to the image nature.

A Nearly Optimum Generalized Multilevel Block Truncation Coding Algorithm with a Fast Nonexhaustive Search Based on a Mean Square Error Criterion

Block Truncation Coding (BTC), using a one-bit moment preserving quantizer to quantize a block, is a simple compression method; however, compression with BTC cannot be used to obtain a sufficiently high quality image, especially an image with many edges. In this paper, a new generalized multilevel BTC algorithm, which arbitrarily specifies the number of quantization levels, is developed. In the proposed algorithm, a nearly optimum quantizer is proposed based on a multiplication-free and non- exhaustive search such that the computational complexity is very low. The simulation results indicate that both the computational complexity and the reconstructed image quality obtained using the proposed multilevel BTC algorithm are better than those obtainable with other existing multilevel BTC algorithms.

A novel block truncation coding of color images using a quaternion-moment-preserving principle

Block truncation coding (BTC) is an efficient tool for image compression. To compress color-pixel blocks, a novel color BTC algorithm, called quaternion-moment block truncation coding (QMBTC), is presented. Analytical formulas for QMBTC, whose computation time is on the order of pixel block size, are derived by using quaternion arithmetic and the moment-preserving principle. The proposed color BTC algorithm can adaptively truncate a pixel block into one or two output classes according to the distribution of the color values inside the blocks. The experimental results show that the compression ratio is increased as compared with existing color BTC algorithms, and the picture quality of the reconstructed images is satisfactory. In addition, a post-BTC data compression scheme is proposed to further compress the subimage constructed by reproduction colors of truncated pixel blocks. Using a lookup table to display decoded data, this postprocessing scheme can output images acceptable to human eyes.

IMAGE COMPRESSION SCHEME WITH EMBEDDED MULTILEVEL BLOCK TRUNCATION CODING BASED ON HUMAN VISUAL EFFECT

ABSTRACT In this paper, we first propose a new embedded multilevel block truncation coding (BTC) technique. Unlike Differential pulse code modulation (DPCM), Vector quantization (VQ), and general multilevel BTC algorithms which determine the image quality at a time, the embedded multilevel BTC improves the image quality largely and progressively until obtaining an image with excellent quality. In order to reduce the bit rate efficiently, we propose a perception model and utilize it to develop a pruning algorithm. The pruning algorithm removes the useless information, which the human eyes are not sensitive, generated by the embedded multilevel BTC. The simulation results indicate that the bit rate with the proposed method is much less than that with the DPCM and general multilevel BTC under the same objective criterion, PSNR, or subjective criterion. This paper also shows that the computation complexity of the proposed method is much less than that with VQ under the same high quality reconstructed image.

Nearly optimum multilevel block truncation coding based on a mean absolute error criterion

A new generalized multilevel block truncation coding (BTC) algorithm based on a mean absolute error criterion is proposed. The proposed multilevel BTC algorithm requires only additions/subtractions to segment a block into desired regions. The simulation results indicate that the mean absolute error and peak signal-to-noise ratio (PSNR) of the algorithm is very close to those of the optimum BTC. The performance of the algorithm is superior to other multilevel BTC algorithms that have been proposed, both in image quality and in computational complexity.

EBTC: An economical method for searching the threshold of BTC compression

An economical method for obtaining a nearly-optimal threshold for the block truncation coding (BTC) image compression algorithm is presented. Simulation results show that the PSNR performance of the proposed economic BTC (EBTC) method is very close to that of the optimised BTC (OBTC) algorithm, but EBTC only takes about half the computation time required by the OBTC algorithm.

Compression of Digital Images by Block Truncation Coding: A Survey

Block truncation coding (BTC) is a lossy moment preserving quantization method for compressing digital gray-level images. Its advantages are simplicity, fault tolerance, the relatively high compression efficiency and good image quality of the decoded image. Several improvements of the basic method have been recently proposed in the literature. In this survey we will study the basic algorithm and its improvements by dividing it into three separate tasks; performing quantization, coding the quantization data and cling the bit plane. Each phase of the algorithm will be analyzed separately. On the basis of the analysis, a combined BTC algorithm will be proposed and the comparisons to the standard JPEG algoritbm will be made

A real-time video signal processing chip

A real-time video signal processing chip for video data compression which combines block truncation coding (BTC) and a conventional motion compensation (MC) algorithm is presented. The regularity and simplicity of the BTC algorithm strongly suggest that it is quite suitable for an efficient VLSI implementation. It is shown that the proposed BTC-based system is much better than the conventional system in terms of implementation, complexity, and accuracy. Two benchmark sequences are used as test examples. The clock rate of the designed chip is about 47 MHz, which is suitable for most video applications. >

Interpolative three-level block truncation coding algorithm

Zeng (see ibid., vol.27, p.1126-8, 1991) has described an interpolative two-level block truncation coding (BTC) image compression algorithm. The authors extend the work of Zeng to a three-level interpolative BTC algorithm. Compared to Zeng, the new algorithm gives reconstructed images with substantially higher image quality with no increase in bit rate.

Block truncation coding using Hopfield neural network

The authors extend the analysis of the block truncation coding (BTC) algorithm using a Hopfield neural network (HNN). They show that its performance is suboptimum (in the mean square error sense) and that alternative (non-neural network) BTC algorithms are available with virtually the same performance.

Image compression by variable block truncation coding with optimal threshold

A variable block truncation coding (BTC) algorithm is proposed for image compression. It is shown that there exists an optimal threshold for the quantization in BTC algorithms (fixed and variable) that minimizes the errors. Compared to the fixed BTC (fBTC), the variable BTC (vBTC) gives better performance on all the tested images. The use of vBTC with optimal threshold leads to a reduction of the error in the reconstructed images by almost 40% of the error in the reconstructed images obtained by fBTC. This enhanced performance suggests that the vBTC with optimal threshold is a better alternative to the fixed block truncation coding. >