The goal of this paper is to binarize quick response (QR) codes with basic image processing methods, so that they can be well recognized in extreme cases. In this paper, we avoid the complex computation that comes fro...
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The goal of this paper is to binarize quick response (QR) codes with basic image processing methods, so that they can be well recognized in extreme cases. In this paper, we avoid the complex computation that comes from sophisticated algorithms, and employ block truncation coding (BTC) to simplify the computation to make processing fast. The final results show that complex algorithms are not required in QR code binarization, and the basic image processing algorithm can achieve faster and better processing results.
A novel hybrid image compression method based on quadtree is proposed, involving two techniques: vector quantization (VQ) and cubic B-spline interpolation. Traditional block-based image compression methods, such as VQ...
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A novel hybrid image compression method based on quadtree is proposed, involving two techniques: vector quantization (VQ) and cubic B-spline interpolation. Traditional block-based image compression methods, such as VQ, block truncation coding and others, do not take the relationship of neighbouring blocks into consideration, which results in a limit to their compression rate. To put it another way, if the relationships between neighbouring blocks can be included in the scheme by application of the quadtree technique and the VQ method, as well as cubic B-spline interpolation, which is exactly what is attempted in this paper, the performance of image compression can be improved significantly. According to the experimental results, this method can achieve satisfactory results in terms of the compression rate, while maintaining an acceptable image quality compared with block-based image compression methods.
This paper deals with clock truncationcoding for gray-scale images. An image region is segmented into unequal-sized square blocks according to local luminance variations and luminance values in the block are approxim...
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This paper deals with clock truncationcoding for gray-scale images. An image region is segmented into unequal-sized square blocks according to local luminance variations and luminance values in the block are approximated by triangular plane patches constructed from the luminance values at the four vertices of the block. Computational complexities of the unequal-sized block segmentations are discussed. Segmentations are classified into three types: top-down splitting;bottom-up merging;and generalized split-and-merge method. It is derived from the theoretical model that initial block size in the split-and-merge method for a given image of size 256 x 256 is 17 x 17. Segmentation by accumulated square error is preferred to human visual sensitivity. Experimental results using a standard image have clarified that the segmentation by the accumulated square error improves the quality of approximated image to 1 to 2 dB compared with segmentation by the mean square error. Moreover, it is confirmed that the approximation by triangular plane patches is superior to JPEG for the compression of the images that have much flat areas.
A new algorithm of classified vector quantization based on quadtree segmentation is proposed. It can obtain lower bit rate and overcome edge degradation. Simulation results show that the better subjective image qualit...
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A new algorithm of classified vector quantization based on quadtree segmentation is proposed. It can obtain lower bit rate and overcome edge degradation. Simulation results show that the better subjective image quality can also be obtained when bitrate is lower than 0.25 bpp. Comparing with JPEG, the improvement of the peak signal to-noice ratio (PSNR) is up to 2 dB at the same bit’rate.
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...
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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.
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 modify...
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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 4x4 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.
In the conventional absolute moment block truncation coding (AMBTC) scheme, the block mean value is taken as the threshold for pixel grouping. But, the use of the block mean value is not optimal in AMBTC subject to th...
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In the conventional absolute moment block truncation coding (AMBTC) scheme, the block mean value is taken as the threshold for pixel grouping. But, the use of the block mean value is not optimal in AMBTC subject to the reconstructed image quality. In this paper, two optimal pixel grouping schemes are put forward for AMBTC. The experimental results shows that the proposed schemes can significantly reduce the computational cost in finding optimal grouping of pixels.
This paper presents a simple technique for improving the quality of the halftoning-based block truncation coding (H-BTC) decoded image. The H-BTC is an image compression technique inspired from typical block truncatio...
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This paper presents a simple technique for improving the quality of the halftoning-based block truncation coding (H-BTC) decoded image. The H-BTC is an image compression technique inspired from typical block truncation coding (BTC). The H-BTC yields a better decoded image compared to that of the classical BTC scheme under human visual observation. However, the impulsive noise commonly appears on the H-BTC decoded image. It induces an unpleasant feeling while one observes this decoded image. Thus, the proposed method presented in this paper aims to suppress the occurring impulsive noise by exploiting a deep learning approach. This process can be regarded as an ill-posed inverse imaging problem, in which the solution candidates of a given problem can be extremely huge and undetermined. The proposed method utilizes the convolutional neural networks (CNN) and residual learning frameworks to solve the aforementioned problem. These frameworks effectively reduce the impulsive noise occurrence, and at the same time, it improves the quality of H-BTC decoded images. The experimental results show the effectiveness of the proposed method in terms of subjective and objective measurements.
In this paper, a watermarking scheme, called Majority-Parity-Guided Error-Diffused block truncation coding (MPG-EDBTC), is proposed to achieve with high image quality and embedded capacity. The main problem of traditi...
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ISBN:
(纸本)9780769537443
In this paper, a watermarking scheme, called Majority-Parity-Guided Error-Diffused block truncation coding (MPG-EDBTC), is proposed to achieve with high image quality and embedded capacity. The main problem of traditional BTC is its poor quality over configurations of high compression ratio. To overcome such problem, the extreme pixel values are employed to substitute both high and low means. The quantized error is also compensated by adjusting the neighboring pixels. With these strategies, the image quality and processing efficiency are improved. Moreover, the watermark is embedded by evaluating the parity value in a pre-defined Parity-Check Region (PCR). As seen in the experimental results, the proposed scheme can provide good robustness, image quality, and processing efficiency. Finally, the proposed MPG-EDBTC is extended to embed multiple watermarks and achieves excellent image quality, robustness, and capacity as well. Nowadays, most multimedia is stored in compressed format. It is more appropriate to embed information such as watermarks in compressed domain. The proposed method has been proved to solve effectively the inherent problems in traditional BTC, and provide excellent performance in watermark embedding.
With the emerging multimedia technology, image data has been generated at high volume. It is thus important to reduce the image file sizes for storage and effective communication. block truncation coding (BTC) is a lo...
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ISBN:
(纸本)9781467350891
With the emerging multimedia technology, image data has been generated at high volume. It is thus important to reduce the image file sizes for storage and effective communication. block truncation coding (BTC) is a lossy image compression technique which uses moment preserving quantization method for compressing digital gray level images. Even though this method retains the visual quality of the reconstructed image with good compression ratio, it shows some, artifacts like staircase effect, raggedness, etc. near the edges. A set of advanced BTC variants reported in literature were studied and it was found that though the compression efficiency is good, the quality of the image has to be improved. A modified block truncation coding using max-min quantizer (MBTC) is proposed in this paper to overcome the above mentioned drawbacks. In the conventional BTC, quantization is done based on the mean and standard deviation of the pixel values in each block. In the proposed method, instead of using the mean and standard deviation, an average value of the maximum, minimum and mean of the blocks of pixels is taken as the threshold for quantization. Experimental analysis shows an improvement in the visual quality of the reconstructed image by reducing the mean square error between the original and the reconstructed image. Since this method involves less number of simple computations, the time taken by this algorithm is also very less when compared with BTC.
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