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.

     

An improved SPIHT algorithm for lossless image coding

In this paper, we propose a new wavelet-based lossless image coder that is based on a state-of-the-art algorithm, namely SPIHT (set partitioning in hierarchical trees). An algorithmic modification is introduced in order to increase its efficiency. This consists of adding a new test on direct descendants in the sets of type A to process the parent coefficients that are significant due to their nondirect descendants. Also, new sets of type C are defined to perform a separate sorting of the sets that have insignificant children. The idea behind the second proposition is to remove all tests over the entries (A, B and C) since the number of significant sets is much higher than that of insignificant sets. A number of experiments, carried out on various test images, demonstrates significant improvement over the conventional SPIHT for both greyscale and colour images.     

分组无损图像压缩编码方法

提出了一种分组无损图像压缩编码方法,该方法预先确定码字,码字由组号和组内码字表示两部分组成,每组中包含了2n个组内码字,组内码字长度不等.灰度级根据概率大小重新排序,与码字一一对应,得到新的编码表,由此达到图像压缩的目的.将此压缩编码方法与Huffman编码进行比较,比较结果表明,该方法在压缩比和编解码效率等方面都要优于Huffman编码.     

An improved lossless data hiding scheme based on image VQ-index residual value coding

Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding.     

哈夫曼编码在图像无损压缩中的应用

针对大学计算机基础课程中信息编码与数据表示知识点,设计了一个使用哈夫曼编码实现图像无损压缩的案例,经过实际课堂实施的检验,表明案例对帮助学生理解信息编码和压缩有很好的效果。     

HEVC-based lossless intra coding for efficient still image compression

Latest advancements in capture and display technologies demand better compression techniques for the storage and transmission of still images and video. High efficiency video coding (HEVC) is the latest video compression standard developed by the joint collaborative team on video coding (JCTVC) with this objective. Although the main design goal of HEVC is the compression of high resolution video, its performance in still image compression is at par with state-of-the-art still image compression standards. This work explores the possibility of incorporating the efficient intra prediction techniques employed in HEVC into the compression of high resolution still images. In the lossless coding mode of HEVC, sample- based angular intra prediction (SAP) methods have shown better prediction accuracy compared to the conventional block-based prediction (BP). In this paper, we propose an improved sample-based angular intra prediction (ISAP), which enhances the accuracy of the highly crucial intra prediction within HEVC. The experimental results show that ISAP in lossless compression of still images outclasses archival tools, state-of-the-art image compression standards and other HEVC-based lossless image compression codecs.     

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.     

基于ROI的医学图像无损压缩

如何高效地压缩医学图像,以便减少存储空间和传输时间,已经成为迫切需要解决的问题。在分析现有的图像压缩方法和医学图像特点的基础上,针对医学图像有损压缩方法和无损压缩方法各自的不足,研究了一种基于ROI（Region of Interest）的医学图像无损压缩方法。     

Near lossless compression of hyperspectral images based on distributed source coding

Effective compression technique of on-board hyperspectral images has been an active topic in the field of hyperspectral remote sensintg.In order to solve the effective compression of on-board hyperspectral images,a new distributed near lossless compression algorithm based on multilevel coset codes is proposed.Due to the diverse importance of each band,a new adaptive rate allocation algorithm is proposed,which allocates rational rate for each band according to the size of weight factor defined for hyperspectral images subject to the target rate constraints.Multiband prediction is introduced for Slepian-Wolf lossless coding and an optimal quantization algorithm is presented under the correct reconstruction of Slepian-Wolf decoder,which minimizes the distortion of reconstructed hyperspectral images under the target rate.Then Slepian-Wolf encoder exploits the correlation of the quantized values to generate the final bit streams.Experimental results show that the proposed algorithm has both higher compression efficiency and lower encoder complexity than several existing classical algorithms.     

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.     

无损自适应分布式算术编码的研究及应用

目前分布式算术编码研究都是基于先验概率已知的有损压缩,为了实现概率自适应的无损压缩,研究了采用结束字符和概率自适应的编码方式来实现编码,提出了无损自适应分布式算术编码。实验结果表明,该算法拥有更好的压缩效果和更低的解码复杂度,并且在实际应用中,编解码可以同时进行。由于无损自适应分布式算术编码具有编码简单、压缩效果好的优点,故将它和比特面编码结合实现超光谱图像压缩,并将仿真结果与3D-SPECK算法比较,结果表明了该方法可以使信噪比提高0.13-0.37dB。     

一种新的无损图像压缩算法的研究

提出了新的二进制(位级)无损图像压缩方法——将错误纠正BCH码引入到图像压缩算法中;将图像的二进制分为大小为7的码字,这些块进入到BCH解码器,消除了校验位后,使得原来的块的大小减少到4位。实验结果表明,此压缩算法是有效的,并给出了一个很好的压缩比,而且不丢失数据。BCH码的使用在提高压缩比方面比单纯霍夫曼压缩的结果要好。     

一种改进的医学图像压缩编码算法

提出了一种基于四叉树分割的分形图像编码的改进算法,通过调整父块与子块的误差来控制编码速度和解码质量。仿真实验表明,该算法有效地提高了图像编码的效率,具有可行性。     

基于改进SPIHT的医学图像编码

考虑到压缩效率很高的静态图像压缩算法（Set Patitioning in Hierarchical Trees,SPIHT）的压缩效率尚可以进一步提高,提出一种改进的SPIHT算法：在原始算法的基础上,引入一种新的类型树,在初始化时最大限度地保存小波变换后的系数．将改进的SPIHT算法应用到医学图像的压缩中,取得良好的压缩效果．     

基于位平面的超光谱图像无损压缩算法的实现

分析了超光谱遥感图像的特征,根据它对压缩算法的特殊要求,提出了基于位平面的无损压缩算法.对于相关性较高的高位位平面采用计算位平面的差值矩阵直接去相关,而相关性较差的低位位平面则采用四叉树划分的方法重组各个像块的大小、位置、灰度信息,从而得到图像的混合编码.实验结果表明,该算法与其它常用无损压缩算法压缩比相当,但压缩时间提高了50%左右.该算法简单实用,适合有实时性要求的超光谱遥感图像压缩.     

Color image compression based on block truncation coding using pattern fitting principle

This paper describes a color image compression technique based on block truncation coding using pattern fitting (BTC-PF). High degree of correlation between the RGB planes of a color image is reduced by transforming them to O₁O₂O₃ planes. Each O_i plane (1?i?3) is then encoded using BTC-PF method. Size of the pattern book and the block size are selected based on the information content of the corresponding plane. The result of the proposed method is compared with that of several BTC based methods and the former is found superior. Though this method is a spatial domain technique, it is also compared with JPEG compression method, which is one of most popular frequency domain techniques. It is found that the performance of the proposed method is a little inferior to that of the JPEG in terms of quality of the reconstructed image. Decoding time is another important criterion where the compressed image is decoded frequently for various purposes. As the proposed method requires negligible decoding time compared to JPEG, the former is preferred over the latter in those cases.     

An improved no-search fractal image coding method based on a fitting plane

In this paper a fast and efficient no-search fractal image coding method based on a modified gray-level transform which uses a fitting plane is presented. The improved gray-level transform can reduce the minimum matching error between a given range block and its corresponding domain block, and thus, it can enhance the possibility of successful domain-range matching. In comparison with our previous scheme which uses an adaptive plane, the improved scheme results in a considerable acceleration of the encoding process, decreases the compression ratio and improves the quality of the reconstructed images in the meanwhile. Comparing with Furao’s no-search scheme, our improved scheme can get higher PSNR at higher bpp and almost the same PSNR at lower bpp. Although it uses more transform coefficients, such a fitting plane method can speed up the encoding process with the quality of the reconstructed images improved.     

An encoding method for both image compression and data lossless information hiding

Reversible image data hiding technology means the cover image can be totally recovered after the embedded secret data is extracted. In this paper, we propose a reversible image data hiding scheme based on vector quantization (VQ) compressed images. The secret bits are embedded into the VQ index table by modifying the index value according to the difference of neighboring indices. The data hiding capacity and the size of the final codestream (embedded result) are a trade-off, and it can be decided by users. In other words, the proposed scheme has flexible hiding capacity ability. To estimate the performance, the proposed scheme was compared with the scheme proposed by Wang and Lu (2009). The results of the comparison showed that our proposed scheme is superior to the scheme proposed by Wang and Lu in both data hiding capacity and bit rate.     

Low-complexity two-step lossless depth coding using coarse Lossy coding

Texture and depth images are generally used for 3D viewing with advanced displays. Because sthe characteristics of a depth image are very different from those of a texture image, an efficient compression method is required to transmit a depth image in a limited bandwidth. In this paper, a low-complexity two-step lossless depth coding (LTLDC) method using coarse lossy coding is proposed. The proposed method downsamples an original image and then coarsely compresses the downsampled image in the first step. This compressed image is upsampled, and then its residual is generated by subtracting the upsampled image from the original image. In the second step, each coding block within the residual and original images is adaptively compressed with a fast mode decision method in a lossless way, and the proposed method determines the best block based on their coding performance. Experimental results show that the proposed LTLDC method achieves a bitrate reduction of 4.35% with encoding complexity reduction of 20.38%.