A fast and low memory image coding algorithm based on lifting wavelet transform and modified SPIHT

Due to its excellent rate–distortion performance, set partitioning in hierarchical trees (SPIHT) has become the state-of-the-art algorithm for image compression. However, the algorithm does not fully provide the desired features of progressive transmission, spatial scalability and optimal visual quality, at very low bit rate coding. Furthermore, the use of three linked lists for recording the coordinates of wavelet coefficients and tree sets during the coding process becomes the bottleneck of a fast implementation of the SPIHT. In this paper, we propose a listless modified SPIHT (LMSPIHT) approach, which is a fast and low memory image coding algorithm based on the lifting wavelet transform. The LMSPIHT jointly considers the advantages of progressive transmission, spatial scalability, and incorporates human visual system (HVS) characteristics in the coding scheme; thus it outperforms the traditional SPIHT algorithm at low bit rate coding. Compared with the SPIHT algorithm, LMSPIHT provides a better compression performance and a superior perceptual performance with low coding complexity. The compression efficiency of LMSPIHT comes from three aspects. The lifting scheme lowers the number of arithmetic operations of the wavelet transform. Moreover, a significance reordering of the modified SPIHT ensures that it codes more significant information belonging to the lower frequency bands earlier in the bit stream than that of the SPIHT to better exploit the energy compaction of the wavelet coefficients. HVS characteristics are employed to improve the perceptual quality of the compressed image by placing more coding artifacts in the less visually significant regions of the image. Finally, a listless implementation structure further reduces the amount of memory and improves the speed of compression by more than 51% for a 512×512 image, as compared with that of the SPIHT algorithm.     

Image coding using lapped biorthogonal transform

The wireless sensor network utilizes image compression algorithms like JPEG, JPEG2000, and SPIHT for image transmission with high coding efficiency. During compression, discrete cosine transform (DCT)–based JPEG has blocking artifacts at low bit-rates. But this effect is reduced by discrete wavelet transform (DWT)–based JPEG2000 and SPIHT algorithm but it possess high computational complexity. This paper proposes an efficient lapped biorthogonal transform (LBT)–based low-complexity zerotree codec (LZC), an entropy coder for image coding algorithm to achieve high compression. The LBT-LZC algorithm yields high compression, better visual quality with low computational complexity. The performance of the proposed method is compared with other popular coding schemes based on LBT, DCT and wavelet transforms. The simulation results reveal that the proposed algorithm reduces the blocking artifacts and achieves high compression. Besides, it is analyzed for noise resilience.     

基于3维SPIHT编码的超光谱图像压缩

提出一种针对超光谱图像压缩的3维SPIHT编码算法.通过对超光谱图像进行3维小波变换,同时去除像素数据间的空间冗余和谱间冗余.针对变换后得到的小波系数,构造一种3维空间方向树结构,并用经3维扩展后的SPIHT算法(3D SPIHT算法)对小波系数进行量化编码.实验证明,基于3维小波变换的3维SPIHT编码算法在对超光谱图像压缩时,表现出了优良的率失真性能.并且算法复杂度适中,具有嵌入式特性.     

小波变换在图像数据压缩中的应用

小波变换的图像编码方法,不仅拥有传统编码的优点,能够消除图像中的统计冗余,并且,其多分辨率的特性提供了消除非统计冗余信息的良好机制。基于离散小波变换(DWT)理论,介绍了DWT在数字图像压缩中的应用,使用零树编码实现了数字图像压缩,并同时保持原图像在各种分辨率下的精细结构,该方法对消除图像中非统计冗余信息提供了有效途径。     

A low memory zerotree coding for arbitrarily shaped objects

The set partitioning in hierarchical trees (SPIHT) algorithm is a computationally simple and efficient zerotree coding technique for image compression. However, the high working memory requirement is its main drawback for hardware realization. We present a low memory zerotree coder (LMZC), which requires much less working memory than SPIHT. The LMZC coding algorithm abandons the use of lists, defines a different tree structure, and merges the sorting pass and the refinement pass together. The main techniques of LMZC are the recursive programming and a top-bit scheme (TBS). In TBS, the top bits of transformed coefficients are used to store the coding status of coefficients instead of the lists used in SPIHT. In order to achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. A compact emplacement of the transformed coefficients is also proposed to further reduce working memory. The LMZC carefully treats "don't care" nodes in the wavelet tree and does not use bits to code such nodes. Comparison of LMZC with SPIHT shows that for coding a 768 /spl times/ 512 color image, LMZC saves at least 5.3 MBytes of memory but only increases a little execution time and reduces minor peak signal-to noise ratio (PSNR) values, thereby making it highly promising for some memory limited applications.     

A wavelet implementation of the pioneering block-based disparity compensated predictive coding algorithm for stereo image pair compression

In this paper, we propose a novel, discrete wavelet transform (DWT) domain implementation of our previously proposed, pioneering block-based disparity compensated predictive coding algorithm for stereo image compression. Under the present research context we perform predictive coding in the form of pioneering block search in the sub-band domain. The resulting transform domain predictive error image is subsequently converted to a so-called wavelet-block representation, before being quantized and entropy coded by a JPEG-like CODEC. We show that the proposed novel implementation is able to effectively transfer the inherent advantages of DWT-based image coding technology to efficient stereo image pair compression. At equivalent bit rates, the proposed algorithm achieves peak signal to noise ratio gains of up to 5.5 dB, for reconstructed predicted images, as compared to traditional and state of the art DCT and DWT-based predictive coding algorithms.     

Sampling adaptive block compressed sensing reconstruction algorithm for images based on edge detection

In this paper, a sampling adaptive for block compressed sensing with smooth projected Landweber based on edge detection （SA-BCS-SPL-ED） image reconstruction algorithm is presented. This algorithm takes full advantage of the characteristics of the block compressed sensing, which assigns a sampling rate depending on its texture complexity of each block. The block complexity is measured by the variance of its texture gradient, big variance with high sampling rates and small variance with low sampling rates. Meanwhile, in order to avoid over-sampling and sub-sampling, we set up the maximum sampling rate and the minimum sampling rate for each block. Through iterative algorithm, the actual sampling rate of the whole image approximately equals to the set up value. In aspects of the directional transforms, discrete cosine transform （DCT）, dual-tree discrete wavelet transform （DDWT）, discrete wavelet transform （DWT） and Contourlet （CT） are used in experiments. Experimental results show that compared to block compressed sensing with smooth projected Landweber （BCS-SPL）, the proposed algorithm is much better with simple texture images and even complicated texture images at the same sampling rate. Besides, SA-BCS-SPL-ED-DDWT is quite good for the most of images while the SA-BCS-SPL-ED-CT is likely better only for more-complicated texture images.     

Real-time implementation of a new low-memory SPIHT image coding algorithm using DSP chip

Among all algorithms based on wavelet transform and zerotree quantization, Said and Pearlman's (1996) set partitioning in hierarchical trees (SPIHT) algorithm is well-known for its simplicity and efficiency. This paper deals with the real-time implementation of SPIHT algorithm using DSP chip. In order to facilitate the implementation and improve the codec's performance, some relative issues are thoroughly discussed, such as the optimization of program structure to speed up the wavelet decomposition. SPIHT's high memory requirement is a major drawback for hardware implementation. In this paper, we modify the original SPIHT algorithm by presenting two new concepts-number of error bits and absolute zerotree. Consequently, the memory cost is significantly reduced. We also introduce a new method to control the coding process by number of error bits. Our experimental results show that the implementation meets common requirement of real-time video coding and is proven to be a practical and efficient DSP solution.     

一种改进的无表零树编码的图像压缩方法

本文对SPIHT算法进行了改进,得到一种在编码和解码过程中都不用链表的小波零树编码方法,称为改进的无表零树编码.这种编码方法大大地减少了内存消耗,降低了编码复杂度,提高了编码速度.实验表明,在相同压缩比下本文算法比LZC算法提高了0.66dB,因此为硬件实现图像压缩编码提供了一种有效的方法.     

改进的SPIHT算法

SPIHT算法是一种高效的嵌入式的零树编码算法,然而,它需要大量的内存空间,不利于DSP或VLSI的实现。LZC算法可以极大地降低编解码器的内存需求,但同时也降低了编解码器的性能。该文利用LZC算法的思想,改进了原来的SPIHT算法,使得在仅仅在LZC算法的内存需求基础上,达到SPIHT算法的性能要求。同时又提出了一种近似搜索算法来提高编码器的速度。     

一种新的自适应数字水印算法

为了提高数字水印抗击各种图像攻击的性能和保持图像的稳健性和不可见性,提出了一种基于离散小波变换(DWT),SVD(singular value decomposition)奇异值分解水印图像和原始载体图像的离散余弦变换(DCT)的自适应水印嵌入算法,主要是将水印图像的两次小波变换后的低频分量潜入到原始图像分块经过SVD分解的S分量矩阵中,同时根据图像的JPEG压缩比的不同计算各个图像块的水印调节因子。实验证明该算法在抗击JPEG压缩、中值滤波、加噪等均具有很好的鲁棒性,嵌入后的图像的PSNR达到38,具有良好的视觉掩蔽性     

基于内存优化的小波零块嵌入图像编码算法

小波嵌入零块编码算法(Set Partitioned Embedded bloCK,SPECK)是一种高效的具有渐进传输特性的图像编码算法,但其在编解码过程中的巨大内存占用致使编解码速度慢且不利于硬件实现.本文提出了一种基于内存优化的小波零块嵌入图像编码算法,采用标志状态图和块深度优先搜索策略完成嵌入编码中的排序和细化过程.两张标志状态图分别标识编解码过程中的重要系数和不重要集合,同时结合块深度优先搜索策略检索块结构中的不重要集合,代替排序和细化过程中的重要系数链表和不重要集合链表,较大地节省了内存占用.实验结果表明,与SPECK算法相比,在保持相当信噪比的情况下,内存占用仅为原来的1/12,与另一种低内存零树编码算法LZC(Listless Zerotree Coding)相比,在内存略有增加的情况下,信噪比至少提高1.1dB,为硬件实现小波零块编码算法探讨了一条有效途径.     

Efficient common-core lossless and lossy image coder based on integer wavelets

The integer wavelet transform (IWT), applied to lossy-image compression, yields rate-distortion performance which is generally, slightly inferior to the discrete wavelet transform (DWT). In this paper, we propose a simple method which, based on the analysis of the IWT signal representation mechanism, is able to raise the IWT performance up to the DWT level. The method can be profitably employed for the implementation of a truly common-core lossless and lossy compression algorithm, with negligible loss of performance with respect to the classical, real-valued transform.     

A modified-set partitioning in hierarchical trees algorithm for real-time image compression

Among all algorithms based on wavelet transform and zerotree quantization, Said and Pearlman’s set partitioning in hierarchical trees (SPIHT) algorithm is well known for its simplicity and efficiency. SPIHT’s high memory requirement is a major drawback to hardware implementation. In this study, we present a modification of SPIHT named modified SPIHT (MSPIHT), which requires less execution time at a low bit rate and less working memory than SPIHT. The MSPIHT coding algorithm is modified with the use of one list to store the coordinates of wavelet coefficients instead of three lists of SPIHT; defines two terms, number of error bits and absolute zerotree; and merges the sorting pass and the refinement pass together as one scan pass. Comparison of MSPIHT with SPIHT on different test image shows that MSPIHT reduces execution time at most 7 times for coding a 512 × 512 grayscale image; reduces execution time at most 11 times at a low bit rate; saves at least 0.5625 MB of memory; and reduces minor peak signal-to noise ratio (PSNR) values, thereby making it highly promising for real-time and memory limited mobile communications. Published in Russian in Radiotekhnika i Elektronika, 2008, Vol. 53, No. 6 pp. 676–685. The text was submitted by the authors in English.     

Region-based wavelet coding methods for digital mammography

Spatial resolution and contrast sensitivity requirements for some types of medical image techniques, including mammography, delay the implementation of new digital technologies, namely, computer-aided diagnosis, picture archiving and communications systems, or teleradiology. In order to reduce transmission time and storage cost, an efficient data-compression scheme to reduce digital data without significant degradation of medical image quality is needed. In this study, we have applied two region-based compression methods to digital mammograms. In both methods, after segmenting the breast region, a region-based discrete wavelet transform is applied, followed by an object-based extension of the set partitioning in hierarchical trees (OB-SPIHT) coding algorithm in one method, and an object-based extension of the set partitioned embedded block (OB-SPECK) coding algorithm in the other. We have compared these specific implementations against the original SPIHT and the new standard JPEG 2000, both using reversible and irreversible filters, on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bit per pixel (bbp). Distortion was evaluated for all images and compression rates by the peak signal-to-noise ratio. For all images, OB-SPIHT and OB-SPECK performed substantially better than the traditional SPIHT and JPEG 2000, and a slight difference in performance was found between them. A comparison applying SPIHT and the standard JPEG 2000 to the same set of images with the background pixels fixed to zero was also carried out, obtaining similar implementation as region-based methods. For digital mammography, region-based compression methods represent an improvement in compression efficiency from full-image methods, also providing the possibility of encoding multiple regions of interest independently.     

Video compression of coronary angiograms based on discrete wavelet transform with block classification

A new method of video compression for angiographic images has been developed to achieve high compression ratio (~20:1) while eliminating block artifacts which leads to loss of diagnostic accuracy. This method adopts motion picture experts group's (MPEGs) motion compensated prediction to takes advantage of frame to frame correlation. However, in contrast to MPEG, the error images arising from mismatches in the motion estimation are encoded by discrete wavelet transform (DWT) rather than block discrete cosine transform (DCT). Furthermore, the authors developed a classification scheme which label each block in an image as intra, error, or background type and encode it accordingly. This hybrid coding can significantly improve the compression efficiency in certain eases. This method can be generalized for any dynamic image sequences applications sensitive to block artifacts.     

Psychophysically derived quantisation model for efficient DWT imagecoding

An efficient method for the optimum quantisation of the luminance and chrominance components for discrete wavelet transform (DWT) still image coding, based on current psychophysical evidence, is described. Its practical application to image compression is presented. This new approach is further supported by experimental work which compares perceived DWT image quality with conventional JPEG-encoding, that is block-based discrete cosine transform (DCT) coding, on the basis of quantisation distortion     

基于DSP Builder的二维提升小波变换实现

离散小波变换在图像压缩处理中有着重要的作用,并得到了广泛的应用.与传统的基于卷积的架构相比较,基于提升的架构具有需要较少的硬件资源,占用较少的芯片面积等优点.在DSP Builder中实现了基于提升的一维离散小波变换,并通过构造相关的存储器控制逻辑,完成了二维离散小波变换架构的设计.利用该架构对图像进行离散小波变换,与软件变换的结果相比较,并计算出图像的峰值信噪比,验证了其正确性.     

Performance improvement of the SPIHT coder

The set partitioning in hierarchical trees (SPIHT) coder is one of the state-of-the-art coders among the wavelet-based image compression coders. For improving the performance of the SPIHT coder, in this paper, we propose a pre-processing method that applies the discrete sine transform or the discrete cosine transform to the wavelet coefficients in the highest frequency subbands and in the next highest frequency subbands before the SPIHT encoding. Experimental results show that the proposed method increases the peak signal to noise ratio by up to 0.4 (dB) in textured images over the original SPIHT coder.     

一种基于Contourlet的无表零树图像编码算法

该文提出一种低存储量、高压缩性能的基于Contourlet变换的无表零树编码算法。通过对Contourlet系数的重新组合,该算法采用类似LZC的零树结构;为了充分利用Contourlet变换对二维分段光滑函数的良好表达能力,设计了不同于LZC 算法的扫描顺序和标志图表示,降低了编解码复杂度。实验结果表明,该算法用于图像编码是有效的,在相同压缩比下,其主观视觉质量和PSNR都明显优于基于小波变换的LZC和SPIHT算法,尤其对于含有丰富纹理的图像。