In this study, an improved block truncation coding (BTC) image compression scheme, namely Near-Aperiodic Dot-Diffused BTC (NADDBTC), is described. Firstly, the existing regular structures for the generation of bitmap ...
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ISBN:
(纸本)9781479983919
In this study, an improved block truncation coding (BTC) image compression scheme, namely Near-Aperiodic Dot-Diffused BTC (NADDBTC), is described. Firstly, the existing regular structures for the generation of bitmap are completely modified for aperiodic compressed results. Moreover, an adaptive quantization levels selection strategy and two parameters Class Matrix (CM) and Diffused Matrix (DM) for image compression are developed and co-optimized. The improvements produce results of superior image quality. Furthermore, the adaptive quantization levels are introduced for balanced false contour, impulsive noise, and blocking artifact. Experimental results demonstrate that the proposed NADDBTC is capable of providing excellent image quality and visual perception, as well as processing efficiency, similar to DDBTC by exploiting the innate parallelism advantage of dot diffusion.
There have been an increasing utilization and consumption of audio files in various applications across different desktop and mobile devices. Thus, audio data compression has been beneficial in reducing the bandwidth ...
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There have been an increasing utilization and consumption of audio files in various applications across different desktop and mobile devices. Thus, audio data compression has been beneficial in reducing the bandwidth and disk space requirements of such data. block truncation coding(BTC), a well-known data compression technique for digital images, was used as the underlying algorithm in encoding audio data. A new encoding technique for audio data was proposed with the following components:(1) quadtree for audio block segmentation,(2) AMBTC for computation of representative values, and(3) Huffman coding for lossless data representation. The performance of the proposed encoding technique was benchmarked and measured using Peak Signal-to-Noise Ratio(PSNR) and compression rate.
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....
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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 ail 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 0.9 dB in average compared to the conventional VQ-BTC algorithms.
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 loca...
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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.
In this paper, a novel detection and recovery system is proposed for image authentication based on two compression schemes: block truncation coding and vector quantization. In order to reduce the overhead in the commu...
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In this paper, a novel detection and recovery system is proposed for image authentication based on two compression schemes: block truncation coding and vector quantization. In order to reduce the overhead in the communication, the sender first compresses an image and then refines the characteristic values from the compressed image. The characteristic values are encrypted to digital signatures using the RSA scheme (proposed by Rivest, Shamir and Adleman). The signatures are embedded into the compressed image and thus the image integrity can be authenticated after transmission. Upon receipt of the compressed image, the receiver extracts the signatures and then decrypts the characteristic values using the sender's public key. Another characteristic value can also be decoded by the image compression scheme. By comparing a few characteristic values, the system determines whether or not the decompressed image is tampered and then outputs a correct characteristic value to rebuild the tampered regions. Furthermore, the proposed scheme authenticates the integrity of the image to the receiver and effectively recovers the tampered image.
In this paper, a new scheme for designing multilevel ETC coding is proposed. Optimal quantization can be obtained by selecting the quantization threshold with an exhaustive search. However, this requires an enormous a...
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In this paper, a new scheme for designing multilevel ETC coding is proposed. Optimal quantization can be obtained by selecting the quantization threshold with an exhaustive search. However, this requires an enormous amount of computation and is, thus impractical when we consider an exhaustive search for the multilevel ETC. In order to find a better threshold so that the average mean square error between the original and reconstructed images is a minimum, the genetic algorithm is applied. Comparison of the results of the proposed method with the exhaustive search reveal that the former method can almost achieve optimal quantization with much less computation than that required in the latter case.
block truncation coding uses a two-level moment preserving quantizer that adapts to local properties of the images. It has the features of low computation load and low memory requirement while its bit rate is only 2.0...
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block truncation coding uses a two-level moment preserving quantizer that adapts to local properties of the images. It has the features of low computation load and low memory requirement while its bit rate is only 2.0 bits per pixel. A more efficient algorithm, the absolute moment ETC (AMBTC) has been extensively used in the field of signal compression because of its simple;computation and better MSE performance. We propose postprocessing methods to further reduce the entropy of two output data of AMBTC, including the bit map and two quantization data (a, b). A block of a 2x4 bit map is packaged into a byte-oriented symbol. The entropy can be reduced from 0.965 bpp to 0.917 bpp on average for our test images. The two subimages of quantization data (a, b) are postprocessed by the Peano Scan. This postprocess can further reduce differential entropy about 0.4 bit for a 4x4 block. By applying arithmetic coding, the total bit reduction is about 0.3 similar to 0.4 bpp. The bit rate can reach 1.6 similar to 1.7 bpp with the same quality as traditional AMBTC.
It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process im...
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It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb-Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 x 4 block are achieved. An image is divided into 4 x 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb-Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 mu m CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 mu m(2) and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression.
In this paper, a novel detection and recovery system is proposed for image authentication based on two compression schemes: block truncation coding and vector quantization. In order to reduce the overhead in the commu...
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In this paper, a novel detection and recovery system is proposed for image authentication based on two compression schemes: block truncation coding and vector quantization. In order to reduce the overhead in the communication, the sender first compresses an image and then refines the characteristic values from the compressed image. The characteristic values are encrypted to digital signatures using the RSA scheme (proposed by Rivest, Shamir and Adleman). The signatures are embedded into the compressed image and thus the image integrity can be authenticated after transmission. Upon receipt of the compressed image, the receiver extracts the signatures and then decrypts the characteristic values using the sender's public key. Another characteristic value can also be decoded by the image compression scheme. By comparing a few characteristic values, the system determines whether or not the decompressed image is tampered and then outputs a correct characteristic value to rebuild the tampered regions. Furthermore, the proposed scheme authenticates the integrity of the image to the receiver and effectively recovers the tampered image.
The rich intelligent multimedia systems provide great convenience and efficiency. Unfortunately, it faces a series of security challenges and threats in developing and deploying multimedia ser-vices, such as tampering...
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The rich intelligent multimedia systems provide great convenience and efficiency. Unfortunately, it faces a series of security challenges and threats in developing and deploying multimedia ser-vices, such as tampering, hijacking, and adversarial attacks. Therefore, this paper proposes a dual -embedding framework based on block truncation coding to improve the security of intelligent multimedia systems. First, the signal is decomposed in frequency domain by using approximate translation invariance to obtain multi-layer frequency-domain parameters;then, this paper hides the encrypted data in two layers of low-frequency coefficients through fragile and robust embedding algorithms, respectively. In addition, in order to further improve the security per-formance, this paper adopts the method of block truncation coding to encrypt the embedded data. On the basis of performance analysis, the superiority of this method is illustrated by comparing with the existing methods.
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