基于9/7双正交小波的一种高效矢量量化算法

该文提出一种用于图像压缩的矢量量化算法,该算法用9/7双正交小波对图像进行分解,利用三个方向上各自小波系数之间的相关性,构造符合其特征的跨频带矢量,提高了图像的编码效率和重构质量,同时采用了新的矢量量化技术渐进构造聚类(PCC),实验结果证明,该算法在未熵编码的条件下,获得 PSNR32dB的重构图像,比特率高达 0.141 bpp.而且实现方法十分简单。     

基于BWT和FVQ的极低比特率图像编码算法

该文提出了一种基于双正交小波变换(BWT)和模糊矢量量化(FVQ)的极低比特率图像编码算法。该算法通过构造符合图像小波变换系数特征的跨频带矢量,充分利用了不同频带小波系数之间的相关性,有效地提高了图像的编码效率和重构质量。该算法采用非线性插补矢量量化(NLIVQ)的思想,从大维数矢量中提取小维数的特征矢量,并提出了一种新的模糊矢量量化方法一渐进构造模糊聚类(PCFC)算法用于特征矢量的量化,从而大大提高了矢量量化的速度和码书质量。实验结果证明,该算法在比特率为0．172bpp的条件下仍能获得PSNR＞30dB的高质量重构图像。     

Wavelet tree classification and hybrid coding for image compression

A hybrid coding system that uses a combination of set partition in hierarchical trees (SPIHT) and vector quantisation (VQ) for image compression is presented. Here, the wavelet coefficients of the input image are rearranged to form the wavelet trees that are composed of the corresponding wavelet coefficients from all the subbands of the same orientation. A simple tree classifier has been proposed to group wavelet trees into two classes based on the amplitude distribution. Each class of wavelet trees is encoded using an appropriate procedure, specifically either SPIHT or VQ. Experimental results show that advantages obtained by combining the superior coding performance of VQ and efficient cross-subband prediction of SPIHT are appreciable for the compression task, especially for natural images with large portions of textures. For example, the proposed hybrid coding outperforms SPIHT by 0.38 dB in PSNR at 0.5 bpp for the Bridge image, and by 0.74 dB at 0.5 bpp for the Mandrill image.     

Vector quantization of image subbands: a survey

Subband and wavelet decompositions are powerful tools in image coding because of their decorrelating effects on image pixels, the concentration of energy in a few coefficients, their multirate/multiresolution framework, and their frequency splitting, which allows for efficient coding matched to the statistics of each frequency band and to the characteristics of the human visual system. Vector quantization (VQ) provides a means of converting the decomposed signal into bits in a manner that takes advantage of remaining inter and intraband correlation as well as of the more flexible partitions of higher dimensional vector spaces. Since 1988, a growing body of research has examined the use of VQ for subband/wavelet transform coefficients. We present a survey of these methods.     

ATM网络环境下的图象编码

文章研究了ＡＴＭ网络环境下的图象压缩编码技术。给出了ＡＴＭ网络的图象编码方案,它是以基于小波变换的分层编码和基于遗传算法的矢量量化技术为基础,其中主要讨论了可变比特率码流的产生,优先级的分配和信元的构成,丢失信元的补偿技术。     

A New Quality Improving Scheme for VQ Decompressed Images Based on DWT

The better compression rate can be achieved by the traditional vector quantization (VQ) method, and the quality of the recovered image can also be accepted. But the decompressed image quality can not be promoted efficiently, so how to balance the image compression rate and image recovering quality is an important issue. In this paper, an image is transformed by discrete wavelet transform (DWT) to generate its DWT transformed image which can be compressed by the VQ method further. Besides, we compute the values between the DWT transformed image and decompressed DWT transformed image as the difference matrix which is the adjustable basis of the decompressed image quality. By controlling the deviation of the difference matrix, there can be nearly lossless compression for the VQ method. Experimental results show that when the number of compressed bits by our method is equal to the number of those bits compressed by the VQ method, the quality of our recovered image is better. Moreover, the proposed method has more compression capability comparing with the VQ scheme.     

Subband Finite-State Vector Quantization for Still Image Coding

Subband coding (SBC) with vector quantization (VQ) has been shown to be an effective method for coding images at low bit rates. The basic idea of subband coding is to split up the frequency band of the signal and then to encode the subbands. Reconstruction is performed by decoding and merging the interpolated subband images. In VQ, the image to be encoded is first processed to yield a set of vectors. The input vectors are individually quantized to the closest codewords in the codebook. In this paper, we propose a new subband finite-state vector quantization (SBC-FSVQ) scheme that combines the SBC and the FSVQ. The frequency band decomposition of an image is carried out by means of 2D separable quadrature mirror filters (QMFs). In our coding scheme, we split the image spectrum into sixteen equally sized subbands. The FSVQ is used to improve the performance by using the correlations of the neighboring samples in the same subband. Thus, our SBC-FSVQ scheme not only has the advantages of the SBC-VQ scheme but also reduces the bit rate and improves the image quality. Experimental results are given and comparisons are made using our new schemes and some other coding techniques. Our technique yields good PSNR performance, for images both inside and outside a training set of five 512 × 512 images. In the experiments, it is found that our SBC-FSVQ scheme achieves the best PSNR performance at nearly the same bit rate.     

A robust joint source channel coding scheme for image transmission over the ionospheric channel

In this paper, we propose a joint source channel coding (JSCC) scheme to the transmission of fixed images for wireless communication applications. The ionospheric channel which presents some characteristics identical to those found on mobile radio channels, like fading, multipath and Doppler effect is our test channel. As this method based on a wavelet transform, a self-organising map (SOM) vector quantization (VQ) optimally mapped on a QAM digital modulation and an unequal error protection (UEP) strategy, this method is particularly well adapted to low bit-rate applications. The compression process consists in applying a SOM VQ on the discrete wavelet transform coefficients and computing several codebooks depending on the sub-images preserved. An UEP is achieved with a correcting code applied on the most significant data. The JSCC consists of an optimal mapping of the VQ codebook vectors on a high spectral efficiency digital modulation. This feature allows preserving the topological organization of the codebook along the transmission chain while keeping a reduced complexity system. This method applied on grey level images can be used for colour images as well. Several tests of transmission for different images have shown the robustness of this method even for high bit error rate (BER>10⁻²). In order to qualify the quality of the image after transmission, we use a PSNR% (peak signal-to-noise ratio) parameter which is the value of the difference of the PSNR after compression at the transmitter and after reception at the receiver. This parameter clearly shows that 95% of the PSNR is preserved when the BER is less than 10⁻².     

Image coding using dual-tree discrete wavelet transform

In this paper, we explore the application of 2-D dual-tree discrete wavelet transform (DDWT), which is a directional and redundant transform, for image coding. Three methods for sparsifying DDWT coefficients, i.e., matching pursuit, basis pursuit, and noise shaping, are compared. We found that noise shaping achieves the best nonlinear approximation efficiency with the lowest computational complexity. The interscale, intersubband, and intrasubband dependency among the DDWT coefficients are analyzed. Three subband coding methods, i.e., SPIHT, EBCOT, and TCE, are evaluated for coding DDWT coefficients. Experimental results show that TCE has the best performance. In spite of the redundancy of the transform, our DDWT _ TCE scheme outperforms JPEG2000 up to 0.70 dB at low bit rates and is comparable to JPEG2000 at high bit rates. The DDWT _TCE scheme also outperforms two other image coders that are based on directional filter banks. To further improve coding efficiency, we extend the DDWT to an anisotropic dual-tree discrete wavelet packets (ADDWP), which incorporates adaptive and anisotropic decomposition into DDWT. The ADDWP subbands are coded with TCE coder. Experimental results show that ADDWP _ TCE provides up to 1.47 dB improvement over the DDWT _TCE scheme, outperforming JPEG2000 up to 2.00 dB. Reconstructed images of our coding schemes are visually more appealing compared with DWT-based coding schemes thanks to the directionality of wavelets.     

一种多分辨率图像混合编码方案

本文提出一种基于小波变换与神经网络的多分辨率图像混合编码方案，利用小波分解对图像的多分辨率表示来消除图像空间域和频率域的相关性，由于小波图像相邻行之间的复杂关系难以用线性表示式来描述，使用多层神经网络（ＭＬＮＮ）来确定这种未知关系。实验证明，神经网络非线性预测器性能优于线性预测器，对非线性预测后的差值图像用自组织特征映射（ＳＯＦＭ）码书进行矢量量化（ＶＱ）编码，编码图像主观质量好，压缩比高，算法简     

Visually improved image compression by combining a conventional wavelet-codec with texture modeling

Human observers are very sensitive to a loss of image texture in photo-realistic images. For example a portrait image without the fine skin texture appears unnatural. Once the image is decomposed by a wavelet transformation, this texture is represented by many wavelet coefficients of low- and medium-amplitude. The conventional encoding of all these coefficients is very bitrate expensive. Instead, such an unstructured or stochastic texture can be modeled by a noise process and be characterized with very few parameters. Thus, a hybrid scheme can be designed that encodes the structural image information by a conventional wavelet codec and the stochastic texture in a model-based manner. Such a scheme, called WITCH (Wavelet-based Image/Texture Coding Hybrid), is proposed. It implements such an hybrid coding approach, while nevertheless preserving the features of progressive and lossless coding. Its low computational complexity and the parameter coding costs of only 0.01 bpp make it a valuable extension of conventional codecs. A comparison with the JPEG2000 image compression standard showed that the WITCH-scheme achieves the same subjective quality while increasing the compression ratio by more than a factor of two.     

基于矢量量化压缩编码的数字水印

提出一种基于矢量量化压缩编码(简称VQ编码)技术的水印策略,在对原图像进行VQ编码后,按码书中码字的相似程度对码字进行划分,根据待嵌入水印图像的大小产生一个随机序列作为密钥,然后根据密钥在压缩数据的特定位置嵌入水印。提出的水印策略,其主要特征在于水印既存在于原图像VQ编码后的压缩数据中,也存在于接收端VQ解码后的图像中。压缩后的数据在数据量上远小于原始数据,所以由它替代原图像携带水印,既节省存储空间,也减小了网络传输时间,特别适用于网络环境下的水印嵌入和提取。更重要的是,这种水印策略具有较好的鲁棒性,能够抵抗诸如裁剪、模糊、JPEG压缩等波形攻击和扭转几何攻击。     

A Region-Based Representation of Images in MARS

We study the problem of representing images within a multimedia Database Management System (DBMS), in order to support fast retrieval operations without compromising storage efficiency. To achieve this goal, we propose new image coding techniques which combine a wavelet representation, embedded coding of the wavelet coefficients, and segmentation of image-domain regions in the wavelet domain. A bitstream is generated in which each image region is encoded independently of other regions, without having to explicitly store information describing the regions. Simulation results show that our proposed algorithms achieve coding performance which compares favorably, both perceptually and objectively, to that achieved using state-of-the-art image/video coding techniques while additionally providing region-based support.     

一种高效的抗误码能力强的图像编码方案

提出了一种新的图像编码方案。对子波变换后的系数进行变系数定长编码,由符号概率表生成字典,并用类似算术编码的方法进行符号串和字典中码字的映射,同时采用一系列的纠错方法以进一步提高方案的抗误码能力。模拟结果表明,该方案与其他方案相比,具有压缩比高,抗误码能力强等优点。     

Popular biorthogonal wavelet filters via a lifting scheme and its application in image compression

A technique using a lifting scheme is presented for constructing compactly supported wavelets whose coefficients are composed of free variables locating in an interval. An efficient approach-based wavelet for image compression is developed by selecting the coefficients of the 9-7 wavelet filter and associated lifting scheme. Furthermore, the rationalised coefficients wavelet filter that can be implemented with simple integer arithmetic is achieved and its characteristic is close to the well known original irrational coefficients 9-7 wavelet filters developed by A. Cohen et al. (Commun. Pure Appl. Maths., vol.45, no.1, p.485-560, 1992). To reduce the computational cost of image coding applications further, an acceleration technique is proposed for the lifting steps. Software and hardware simulations show that the new method has very low complexity, and simultaneously preserves the high quality of the compressed image.     

Fractal curves to improve the reversible data embedding for VQ-indexes based on locally adaptive coding

The VQ reversible data embedding technology allows an original VQ coding to be completely restored after the extraction of embedded data. In this paper, we propose a new reversible scheme based on locally adaptive coding for VQ-compressed images. The fractal Hilbert curve is applied to replace the traditional trace of processing the VQ index table. The VQ index table is pre-processed to create a fractal Hilbert curve. Following the curve to process the VQ index table can get better compression rates in the data embedding procedure. Besides, compared to Chang et al.’s scheme, which compressed the inputted VQ index value only when the to-be-embedded bit b is 0, our method performs compressing operations in both cases that the to-be-embedded bits b are 0 and 1. The experimental results show that the proposed method has the best compression rate and the highest embedding capacity compared with other reversible VQ embedding methods.     

On exploiting geometric constraint of image wavelet coefficients

We investigate the problem of how to exploit the geometric constraints of edges in wavelet-based image coding. The value of studying this problem is the potential coding gain brought by improved probabilistic models of wavelet high-band coefficients. Novel phase shifting and prediction algorithms are derived in the wavelet space. It is demonstrated that after resolving the phase uncertainty, high-band wavelet coefficients can be better modeled by biased-mean probability models rather than the existing zero-mean ones. In lossy coding, the coding gain brought by the biased-mean model is quantitatively analyzed within the conventional DPCM coding framework. Experimental results show that the proposed phase shifting and prediction scheme improves both the subjective and objective performance of wavelet-based image coders.     

Wavelet-based image coding using nonlinear interpolative vectorquantization

We propose a reduced complexity wavelet-based image coding technique. Here, 64-D (for three stages of decomposition) vectors are formed by combining appropriate coefficients from the wavelet subimages, 16-D feature vectors are then extracted from the 64-D vectors on which vector quantization (VQ) is performed. At the decoder, 64-D vectors are reconstructed using a nonlinear interpolative technique. The proposed technique has a reduced complexity and has the potential to provide a superior coding performance when the codebook is generated using the training vectors drawn from similar images.     

Progressive Image Transmission Using LOT with Two-Channel Conjugate VQ

A progressive image transmission (PIT) scheme based on the classified two-channel conjugate VQ (TCCVQ) technique in the lapped orthogonal transform (LOT) domain is proposed. Conventional block transform coding of images using DCT produces in general undesirable block-artifacts at low bit rates. In this paper, image blocks are transformed using the LOT and classified into four classes based on their structural properties and further subdivided adaptively into subvectors depending on the LOT coefficient statistics to improve the reconstructed image quality by adaptive bit allocation. The subvectors are quantized using the two-channel conjugate VQ technique which has less computational complexity and less storage memory, and is more robust against channel errors. The vector quantized subvectors are transmitted and decoded in stages so that an image is progressively reconstructed, i.e., initially a crude version followed by quality build up in successive stages as the occasion demands in interactive visual communications. Coding tests using computer simulations show that the LOT/TCCVQ-based PIT of images is an effective coding scheme. The results are also compared with those obtained using conventional classified VQ in both the DCT and LOT domains.     

Image coding using wavelet transform and classified vectorquantisation

The wavelet transform, which provides a multiresolution representation of images, has been widely used in image compression. A new image coding scheme using the wavelet transform and classified vector quantisation is presented. The input image is first decomposed into a hierarchy of three layers containing ten subimages by the discrete wavelet transform. The lowest resolution low frequency subimage is scalar quantised with 8 bits/pixel. The high frequency subimages are compressed by classified vector quantisation to utilise the crosscorrelation among different resolutions while reducing the edge distortion and computational complexity. Vectors are constructed by combining the corresponding wavelet coefficients of different resolutions in the same orientation and classified according to the magnitude and the position of wavelet transform coefficients. Simulation results show that the proposed scheme has a better performance than those utilising current scalar or vector quantisation schemes