VQ indexes compression and information hiding using hybrid lossless index coding

For the compression of memoryless vector quantization (VQ), most of the lossless index coding algorithms are not suitable for various test images. As a result, we present a hybrid dynamic tree-coding scheme (DTCS) and modified search order coding scheme (MSOC) to re-encode the output index map efficiently without causing any extra coding distortion. The main idea behind this scheme is that the adjacent left and upper around the current processed block usually provide more useful information than its adjacent left-upper and right-upper block, thus we employ two different coding methods according to their corresponding left or upper spatial relations. In addition, we applied the HLIC method to the information hiding. The proposed method does not modify the contents of the secret data and the compressed image. Experimental results show that the newly proposed algorithm achieves significant reduction of bit rate compared to the other lossless index coding schemes for various test images and different codebook sizes. The proposed information hiding scheme can hide a huge amount of information in the index map of an image and allows complete reconstruction of the indexes of the image.     

Lossless data hiding for color images based on block truncation coding

In this paper, we present a novel, reversible steganographic method, which can reconstruct an original image effectively after extracting the embedded secret data. The proposed reversible hiding method aims at BTC (block truncation coding)-compressed color images. Conventionally, each block of a color image compressed by BTC requires three bitmaps and three pairs of quantization levels for reconstruction. In order to improve the compression rate, a genetic algorithm (GA) is applied to find an approximate optimal common bitmap to replace the original three. The secret data then are embedded in the common bitmap and the quantization levels of each block use the properties of side matching and the order of these quantization levels to achieve reversibility. The experimental results demonstrate that the proposed method is practical for BTC-compressed color images and can embed more than three bits in each BTC-encoded block on average.     

Lossless data hiding for absolute moment block truncation coding using histogram modification

This paper presents a lossless data hiding method for an absolute moment block truncation coding (AMBTC) images, which is a compressed grayscale image. It is not easy to hide secret data in an AMBTC-compressed image because it is composed of bit planes. Thus, it is very sensitive to change some pixels. Nevertheless, to improve the hiding capacity, we present an efficient extension of the histogram modification technique by counting the coefficients of the bit planes in each 4 × 4 block. In addition, our proposed scheme has low complexity and achieves a high embedding capacity with the good perceptual quality compared to the prior arts. In an experiment, we verified our proposed data hiding scheme through the comparison with previous methods.     

基于块方向预测和Context的图象无失真编码方法

首先分析无失真图象编码技术,提出一种基于块方向预测和Context的自适应无失真编码方法,该方法主要使用块方向预测和基于Context的误差模型去除图象在空间上的相关性.在此方法中,一幅图象首先被分割成图象块,对图象的每一块自适应地选择一个使预测误差绝对值之和最小的块方向预测器；然后通过Context选择和误差反馈进一步降低信息熵；最后,采用快速而有效的Rice编码器对误差图象编码.实验结果显示，该方法的压缩效果明显优于JPEG(joint of picture expert group)无失真模式和FELICS(fast and efficient lossless image compression),略好于CB9和LOCO-I,甚至UCM(universal context modeling).     

A novel lossless compression framework for facial depth images in expression recognition

With the development of AR and VR, depth images are widely used for facial expression analysis and recognition. To reduce the storage size and save bandwidth, an efficient compression framework is desired. In this paper, we propose a novel lossless compression framework for facial depth images in expression recognition. In the proposed framework, two steps are designed to remove the redundancy in the facial depth images, which are data preparing and bitstream encoding operations. In the data preparing operation, the original image is represented by the same and different parts between the left and right sides. In the bitstream encoding operation, these parts are compressed to get the final bitstream. The proposed framework is implemented and examined on the BU-3DFE Database. Experimental result shows that the proposed technique outperforms existing lossless compression frameworks in terms of compression efficiency, and the average data size is reduced to 25.27% by the proposed framework.

     

一种准无损压缩图象子带编码算法

针对有损压缩会损失一部分信息而无损压缩又压缩比较低的问题,提出一种实现准无损压缩的方法。该方法就是首先将图象用噪声模型进行去除噪声处理,以提高图象的信噪比,并有利于图象的压缩;然后再使用区域自适应子带编码算法进行编码。由于该算法能快速收敛,因而编码时间相对较少;编码通常能实时执行。实验结果表明,该压缩方案具有高信噪比、高压缩比等优良性能。从算法的理论基础来看,其中基于噪声模型的噪声清除算法对其他编码算法（如DCT、DPCM、JPEG、SPIHT、MPEG等等）同样具有推广意义。     

基于静态串表的图像无损压缩编码方法

结合前像素预测编码方法和Lempel-Ziv-Welch(LZW)编码思想并针对其对于变化频率较高的信号压缩效率较低的问题,提出了一种通过利用图像像素之间相关性构建静态串表对数字图像进行无损压缩的编码方法。通过对前向预测编码处理后的图像数据进行查表编码来实现图像无损压缩。实验结果表明该方法实现简单,压缩效率高于LZW算法和WinZIP算法。     

基于DPCM与S+P变换的图像无损压缩

S+P变换是由AmirSaid犤7犦提出的一种多分辨表示方法,能够实现整数到整数的变换,从而成功地应用于图像的无损压缩,其性能优于基于线性预测的JPEG标准。为了进一步提高压缩性能,论文提出了一种基于DPCM与S+P变换的图像无损压缩算法。首先,对原始图像进行线性预测,得到差值图像;其次,对差值图像进行S+P变换;最后,对变换系数进行熵编码压缩。新算法利用像素间的相关性,给出了一种新的利用一维S+P变换实现图像变换的方法,减少了变换增加的数据量,有效地解决了边界处理问题。实验结果与性能比较表明:新算法有效的,优于其它著名的基于多分辨分解的无损图像压缩编码算法犤7,10,12犦。     

An improved lossless image compression algorithm based on Huffman coding

There is an increasing number of image data produced in our life nowadays, which creates a big challenge to store and transmit them. For some fields requiring high fidelity, the lossless image compression becomes significant, because it can reduce the size of image data without quality loss. To solve the difficulty in improving the lossless image compression ratio, we propose an improved lossless image compression algorithm that theoretically provides an approximately quadruple compression combining the linear prediction, integer wavelet transform (IWT) with output coefficients processing and Huffman coding. A new hybrid transform exploiting a new prediction template and a coefficient processing of IWT is the main contribution of this algorithm. The experimental results on three different image sets show that the proposed algorithm outperforms state-of-the-art algorithms. The compression ratios are improved by at least 6.22% up to 72.36%. Our algorithm is more suitable to compress images with complex texture and higher resolution at an acceptable compression speed.

     

A new fast reversible method for image safe transfer

In this paper a novel reversible method for fast and safe image transfer is proposed. The method combines compression, data hiding and partial encryption of images in a single processing step. The proposed approach can embed data into the image according to the message size and partially encrypt the image and the message without changing the original image content. Moreover, during the same process the image is lossless compressed. Nevertheless, the compression rate depends on the upper bound of message size to embed in the image. The main idea is to decompose the original image into two sub-images and to apply various processes to each sub-image in order to gain space and increase the amount of embedded data. The two sub-images are then scrambled and partially encrypted. The most significant characteristic of the proposed method is the utilization of a single procedure to simultaneously perform the compression, the reversible data hiding and the partial encryption rather than using three separate procedures. Our approach reduces then the computational effort and the required computation time. This method is specially suited for medical images where one can associate the patient diagnostic to the concerned medical image for safe transfer purpose.     

A novel high payload steganography scheme based on absolute moment block truncation coding

In this paper, we propose an efficient steganography method in the compressed codes of absolute moment block truncation coding (AMBTC). Many recent related schemes focus on implementing reversible data hiding in compressed AMBTC bit stream. However, the reconstructed image of AMBTC is already lossy and the strict reversibility severely limits embedding capacity. Due to the simplicity and regularity of AMBTC codes, implementing irreversible hiding scheme causes very slight loss visual distortion of reconstructed image in exchange of significant improve in embedding capacity. In proposed scheme, smoothness of AMBTC compressed trio is firstly detected, which is then indicated by substituting the LSB of high quantity level with flag bit. For smooth trios, the differences between both quantity levels are firstly encoded by Huffman coding and then concatenated with secret data to generate modified low quantity levels. Meanwhile, all bits in bit planes of smooth trios are substituted with secret data as well. For complex trio, secret bits are only embedded into quantity levels, which is similar to smooth trio except for the differences are encoded by Lloyd-Max quantization. Experimental results indicate that proposed scheme outperforms prior methods both in imperceptivity and embedding capacity, which confirms the effectiveness and superiority of our work.

     

A new watermarking scheme for colour image using QR decomposition and ternary coding

At present, the binary images are often used as the original watermark images of many watermarking methods, but partial methods cannot be easily extended to colour image watermarking methods. For resolving this problem, we propose a new watermarking method using ternary coding and QR decomposition for colour image. In the procedure of embedding watermark, the colour image watermark is coded to ternary information; the colour host image is also separated into image blocks of sized 3?×?3, and these image blocks are further decomposed via QR decomposition; then, one ternary watermark is embedded into one orthogonal matrix Q of QR decomposition by the proposed rules. In the procedure of extracting watermark, the proposed method uses the blind-manner to extract the embedded ternary information. The novelty of this scheme lies in the proposed ternary coding for watermark image, which can improve the imperceptibility, embedded watermark capacity and real-time feature of the watermarking scheme. The results of simulation show the presented technique is better than other compared schemes with respect to imperceptibility, embedded watermark capacity and real-time feature under the similar robustness.

     

Adaptive and dynamic multi-grouping scheme for absolute moment block truncation coding

Image compression technique is widely used in multimedia signal processing. As a conventional lossy compression technique, block truncation coding (BTC) deserves further improvements to enhance its performance of compression. The improvements of BTC mainly focus on: 1) enhancing the quality of reconstructed image and 2) decreasing the bit rate. In this paper, an adaptive and dynamic multi-grouping scheme is proposed for the absolute moment block truncation coding (AMBTC), which is mainly based on an optimized grouping mechanism with the adaptive threshold setting according to the complexity of image blocks. Besides, the values of the reconstruction levels are replaced by their compressed difference values in order to decrease the bit rate. Experimental results demonstrate that the proposed scheme can enhance the compression performance of AMBTC effectively.

     

抖动半调图像跳黑块无损压缩算法

提出了一种有序抖动半调图像跳黑块无损压缩方法。通过分析抖动半调图像特性,对半调图进行分块及块间异或预处理,使其转变为较大面积黑色区域中夹杂着零星白点的二值图像,接着运用跳黑块编码法对其无损压缩。在跳黑块编码中,对只有一个白像素的非黑块采用特定的短码字编码;对其余类型的非黑块采用直接编码。实验结果表明,改进的跳黑块编码能在一定程度上对非黑块进行有效压缩,且新算法不但具有较高的压缩效率,时间、空间复杂度也较低。     

Low-complexity lossless image coding

A new lossless image coding method competitive with the best known image coding techniques in terms of efficiency and complexity is suggested. It is based on adaptive color space transform, adaptive context coding, and improved prediction of pixel values of image color components. Examples of application of the new algorithm to a set of standard images are given and comparison with known algorithms is performed.     

四叉树分块的高光谱图像分布式无损编码

目的 近年来随着光谱成像技术的快速发展,使得高光谱遥感图像能够提供更加丰富的地物信息,然而其所具有的极大数据量给图像的存储、传输和实用带来较大的困难。因此,如何对高光谱遥感图像进行有效编码成为研究热点。方法 基于陪集码的分布式信源编码因其具有良好的压缩性能和较低的编码复杂度而受到重视,在此基础上提出一种基于自适应四叉树分块的高光谱图像分布式无损编码方案。设每个高光谱帧组的第1帧为关键帧,其他帧为普通帧,首先对关键帧进行自适应四叉树分块,然后对每一块与普通帧相应的块进行最佳线性预测,进而根据预测残差确定所需传输的陪集码索引位数以及每个像素的k个最低比特位。结果 本文通过自适应四叉树分块,增强了所形成陪集码的自适应性。所提出的编码方案很好地实现了压缩效果和编码效率的折中。结论 提出的方案能够较好地适应低复杂度环境下对高光谱图像无损编码的需求。     

Post-processing coding artefacts for JPEG documents

Coding artefacts, including ringing and blocking artefacts, are often introduced when document images are compressed using the JPEG standard. These artefacts severely impact visual perception of the image content. Although a number of methods have been presented to deal with coding artefacts, most of them are dedicated to natural images; few works have investigated to work on document content. The current work is an attempt to fill this lack. In contrast to all the approaches taken by previous works, we propose to post-process the coding artefacts by estimating the quantization noise, which is not available on the decoder’s side. The estimated noise is then used to reconstruct the image with better quality. A number of experiments were conducted to show the efficiency of the proposed method in comparison with the state-of-the-art methods.     

United coding method for compound image compression

This paper proposes a compound image coding method named united coding (UC). In UC, several lossless coding tools such as dictionary-entropy coders, run-length encoding (RLE), Hextile, and a few filters used in portable network graphics (PNG) format are united into H.264 like intraframe hybrid video coding. The basic coding unit (BCU) has a size typically between 16?×?16 pixels to 64?×?64 pixels. All coders in UC are used to code each BCU. Then, the lossless coder that generates minimum bit-rate (R) is chosen as the optimal lossless coder. Finally, the final optimal coder is chosen from the lossy intraframe hybrid coder and the optimal lossless coder using R-D cost based optimization criterion. Moreover, the data coded by one lossless coder can be used as the dictionary of other lossless coders. Experimental results demonstrate that compared with H.264, UC achieves up to 20 dB PSNR improvement and better visual picture quality for compound images with mixed text, graphics and natural picture. Compared with lossless coders such as gzip and PNG, UC can achieve 2–5 times higher compression ratio with just a minor loss and keep partial-lossless picture quality. The partial-lossless nature of UC is indispensable for some typical applications, such as cloud computing and rendering, cloudlet-screen computing and remote desktop, where lossless coding of partial image regions is demanded. On the other hand, the implementation complexity and cost increment of UC is moderate, typically less than 25 % of a traditional hybrid coder such as H.264.     

Double linear regression prediction based reversible data hiding in encrypted images

Existing prediction-based works on reversible data hiding in encrypted images usually embed the secret messages by referring to the difference between current pixel and its predicted value. An accurate prediction model may promote an improvement of embedding capacity. Existing schemes, however, may not work well due to involving a bad prediction model so that their embedding capacity cannot be improved further. To address the problem, this paper proposes a new reversible data hiding scheme in encrypted images by designing double linear regression prediction model. Proposed model can significantly improve the prediction accuracy of current pixel based on neighboring pixels, more auxiliary rooms are thus vacated to embed secret data. Furthermore, a prediction error map is constructed to mark the error positions caused by inaccurate prediction, which can be further compressed lossless to lower the capacity of auxiliary data. Reversible recovery for original image can be finally achieved successfully. Experimental results demonstrate that the proposed scheme significantly improves prediction accuracy and data embedding capacity by combining double linear regression prediction model and prediction error map, and then can achieve separable and lossless recovery for the original image. Compared with existing works, the proposed scheme can implement a higher visual quality of decrypted images, while maintaining a larger embedding capacity.

     

基于颜色聚类的计算机桌面图像压缩算法

针对计算机桌面图像压缩问题,提出一种基于颜色聚类的图像压缩算法。将桌面图像划分成16×16的非重叠块,归为文本/图形块、自然图像块及混合块3类。对色彩丰富文本/图形块进行颜色聚类,以降低块的颜色种类数,并做无损压缩。对自然图像块采用H.264帧内预测编码方法,对混合块采用混合编码方法。实验结果表明,该算法所得图像的峰值信噪比和结构相似度均高于传统算法。