Listless block-tree set partitioning algorithm for very low bit rate embedded image compression

This paper presents a listless implementation of wavelet based block tree coding (WBTC) algorithm of varying root block sizes. WBTC algorithm improves the image compression performance of set partitioning in hierarchical trees (SPIHT) at lower rates by efficiently encoding both inter and intra scale correlation using block trees. Though WBTC lowers the memory requirement by using block trees compared to SPIHT, it makes use of three ordered auxiliary lists. This feature makes WBTC undesirable for hardware implementation; as it needs a lot of memory management when the list nodes grow exponentially on each pass. The proposed listless implementation of WBTC algorithm uses special markers instead of lists. This reduces dynamic memory requirement by 88% with respect to WBTC and 89% with respect to SPIHT. The proposed algorithm is combined with discrete cosine transform (DCT) and discrete wavelet transform (DWT) to show its superiority over DCT and DWT based embedded coders, including JPEG 2000 at lower rates. The compression performance on most of the standard test images is nearly same as WBTC, and outperforms SPIHT by a wide margin particularly at lower bit rates.     

Biorthogonal and nonuniform lapped transforms for transform codingwith reduced blocking and ringing artifacts

New lapped transforms are introduced. The lapped biorthononal transform (LBT) and hierarchical lapped biorthogonal transform (HLBT) are appropriate for image coding, and the modulated HLBT biorthogonal transform (MMLBT) and nonuniform modulated lapped biorthogonal transform (NMLBT) are appropriate for audio coding. The HLBT has a significantly lower computational complexity than the lapped orthogonal transform (LOT), essentially no blocking artifacts, and fewer ringing artifacts than the commonly used discrete cosine transform (DCT). The LBT and HLBT have transform coding gains that are typically between 0.5 and 1.2 dB higher than that of the DCT. Image coding examples using JPEG and embedded zerotree coders demonstrate the better performance of the LET and HLBT. The NMLBT has fewer ringing artifacts and better reproduction of transient sounds than the MLT, as shown in audio coding examples. Fast algorithms for both the HLBT and the NMLBT are presented     

A CMOS Image Sensor for Multi-Level Focal Plane Image Decomposition

An alternative image decomposition method that exploits prediction via nearby pixels has been integrated on the CMOS image sensor focal plane. The proposed focal plane decomposition is compared to the 2-D discrete wavelet transform (DWT) decomposition commonly used in state of the art compression schemes such as SPIHT and JPEG2000. The method achieves comparable compression performance with much lower computational complexity and allows image compression to be implemented directly on the sensor focal plane in a completely pixel parallel structure. A CMOS prototype chip has been fabricated and tested. The test results validate the pixel design and demonstrate that lossy prediction based focal plane image compression can be realized inside the sensor pixel array to achieve a high frame rate with much lower data readout volume. The features of the proposed decomposition scheme also benefit real-time, low rate and low power applications.      

基于DCT变换的内嵌静止图像压缩算法

提出了一种有效的基于离散余弦变换(DCT)的内嵌子带图像编码算法.Xiong等人提出的EZDCT算法采用零树结构实现了一种内嵌DCT编码器,且其性能优于JPEG.本文指出DCT的零树结构在内嵌DCT算法中并非很有效,同时提出了一种不依赖零树结构的简便、高效的内嵌DCT子带编码算法.实验结果表明本文算的压缩性能(PSNR)比EZDCT高约0.5~1.5dB,且接近当前最通用的内嵌小波SPIHT算法,在对某些图像压缩时还优于SPIHT算法.     

基于OpenMP的JPEG2000图像并行编码算法

JPEG2000是新一代图像压缩标准,具有编码效率高、性能好等优点,由于采用小波和比特平面编码技术,其编码复杂度高,编码速度较慢。为了提高JPEG2000的编码速度,提出一种基于OpenMP的JPEG2000图像并行编码算法,通过对离散小波变换和EBCOT算法的并行处理,提高编码速度。结果表明,该算法在保持了JPEG2000良好特性的基础上,大大提高了编码速度,而且图像越大,对编码速度的改善越明显,使JPEG2000更加适用于大数据量的图像的传输。     

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.     

SPIHT静止图像压缩技术研究

JPEG2000的静止图像压缩算法具有很多优良特性,其核心算法是EBCOT,构造复杂、硬件实现难度大。而基于SPIHT算法的图像压缩效率接近EBCOT,但结构简单、易于硬件实现。通过对SPIHT和JPEG2000算法进行融合,提出了一套压缩比高、可实现由有损到无损、码流渐进传输的静止图像编码方案,并对SPIHT算法与(9,7)小波提升算法的融合方法进行分析研究,所构造系统性能与JPEG2000算法接近,具有较好的应用前景。     

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.     

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     

Energy-Efficient 8-Point DCT Approximations: Theory and Hardware Architectures

Due to its remarkable energy compaction properties, the discrete cosine transform (DCT) is employed in a multitude of compression standards, such as JPEG and H.265/HEVC. Several low-complexity integer approximations for the DCT have been proposed for both 1D and 2D signal analyses. The increasing demand for low-complexity, energy-efficient methods requires algorithms with even lower computational costs. In this paper, new 8-point DCT approximations with very low arithmetic complexity are presented. The new transforms are proposed based on pruning state-of-the-art DCT approximations. The proposed algorithms were assessed in terms of arithmetic complexity, energy retention capability, and image compression performance. In addition, a metric combining performance and computational complexity measures was proposed. Results showed good performance and extremely low computational complexity. Introduced algorithms were mapped into systolic-array digital architectures and physically realized as digital prototype circuits using FPGA technology and mapped to 45 nm CMOS technology. All hardware-related metrics showed low resource consumption of the proposed pruned approximate transforms. The best proposed transform according to the introduced metric presents a reduction in power consumption of 21–25 %.     

基于二维APDCSF的列率子带特征编码方法

提出了一种子带编码的新方法。该方法利用二维全相位离散反余弦列率滤波器（APDCSF）对图像进行子带分解;对于低频子带图像采用直接斜交多重亚采样和基于全相位离散反余弦列牢滤波器（APDICsF）的多重旋转内插恢复．而对高频子带图像利用直方图自动阈值化提取如边缘和线等特征的图像元;根据各个子带的图像元的特征分别进行编码压缩,解压缩后利用凸集投影重建原始图像。该方法消除了传统的离散余弦变换（DCT）编码的方块化效应,与基于小波变换的子带特征编码方法相比,计算复杂度小,压缩率高,主观视觉性能好,对于灰阶图像可达到0．1～0．3bpp,特别适用于低比特率图像压缩。     

基于人眼视觉特性的ROI渐进编码算法

一般基于感兴趣区域(ROI)的图像编码算法都没有充分考虑人眼视觉特性,在分析了JPEG2000中感兴趣区域编码的优缺点后,利用小波变换的特点并结合人眼视觉的掩蔽效应,提出了一种改进的ROI图像编码算法。首先将图像小波域中的所有低频系数进行移位处理,而对于小波域中的高频系数,仅对其属于ROI区域的系数进行移位处理,再利用等级树集合分割(SPIHT)算法进行编码传输。仿真实验证明了该算法比原有算法的图像主观质量更好。     

Objective and subjective quality assessment between JPEG XR with overlap and JPEG 2000

JPEG XR (eXtended Range) is a recently standardized format for still images compression. It adopts a Lapped Biorthogonal Transform (LBT) that helps in reducing visual artifacts, in particular those due to blocking effects. In this paper, we compare JPEG XR with JPEG 2000, in terms of both objective and subjective visual quality. The adopted objective parameters are the computational complexity and the PSNR. In order to improve the analysis, and to evaluate if JPEG XR can be a feasible alternative to JPEG 2000, subjective tests in a projector-based environment have been set up, from which the benefits of the overlap operator of JPEG XR have been assessed, especially at high compression ratios.     

一种基于DCT变换的ROI嵌入式编码算法

JPEG是目前应用最广泛的静止图像压缩标准,将JPEG功能多样化具有重要意义。利用DCT变换比小波变换更加简单、快速的特点,结合SPIHT算法细化过程原理,实现了一种与JPEG标准兼容的ROI编码算法,既不增加编码时间,又可以实现ROI的渐进式增强传输,而且在图像重建质量稳定性和可控性方面要远远好于区域自适应量化编码,在较低码率下,仅增加少量码流就能够明显改善主观视觉感受。     

基于DCT变换的渐进式图像编码方法

本文将DCT变换与SPECK算法相结合,提出了一种基于DCT变换的渐进式图像编码方法.该方法充分利用了DCT变换实现简单的特点,还能充分发挥零树编码的优势实现对图像的高效压缩.输出码流具有嵌入的特性,便于码率控制,具有一定的抗误码能力.同时计算复杂度低,比较适合于低复杂度的图像编码与传输.     

用于JPEG2000图像认证的半脆弱性数字水印算法

本文给出一种适用于JPEG2000图像认证的半脆弱性数字水印算法,该算法与JPEG2000编、解码器相结合,它根据JPEG2000图像压缩过程中的不变参量进行水印生成和嵌入调制,利用小波变换特性对图像篡改区域进行定位.实验表明,采用该算法实现的水印不仅具有良好的视觉透明性,而且还有较佳的篡改区域指示功能.     

一种基于低复杂度嵌入零块编码的遥感图像压缩编码方法

该文针对遥感图像的数据特点,提出了一种新的遥感图像编码方法。它基于一种改进的小波变换嵌入零块编码算法。新算法中改进了零块编码中四叉树分裂算法(quadtree),并设计亍新的链表生成和不重要集合排序策略。通过这些方法的改进,不仅提高了图像编码性能,同时还大大提高了运算效率。实验表明该文阐述的算法具有很低的复杂度和高的压缩率,PSNR和计算速度均超过SPIHT和SPECK。在1bpp下,该文方法的PSNR比SPIHT提高了0.3dB以上,计算速度比SPIHT提高了35％。     

Perceptual blur and ringing metrics: application to JPEG2000

We present a full- and no-reference blur metric as well as a full-reference ringing metric. These metrics are based on an analysis of the edges and adjacent regions in an image and have very low computational complexity. As blur and ringing are typical artifacts of wavelet compression, the metrics are then applied to JPEG2000 coded images. Their perceptual significance is corroborated through a number of subjective experiments. The results show that the proposed metrics perform well over a wide range of image content and distortion levels. Potential applications include source coding optimization and network resource management.     

A locally adaptive algorithm for measuring blocking artifacts in images and videos

Block transform coding is the most popular approach for image and video compression. The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video coding systems. This paper presents a new algorithm for measuring image quality of a BDCT coded images or videos. It exhibits unique and useful features: (1) it examines the blocks individually so that it can measure the severity of blocking artifacts locally; (2) it is a one-pass algorithm in the sense that the image needs to be accessed only once; (3) it takes into account the blocking artifacts for high bit rate images and the flatness for the very low bit rate images; (4) the quality measure is well defined in the range of 0–10. Experiments on various still images and videos show that the new quality measure is very efficient in terms of computational complexity and memory usage, and can produce consistent blocking artifacts measurement.     

Image postprocessing by Non-local Kuan’s filter

Blocking artifacts exist in images and video sequences compressed to low bit rates using block-based discrete cosine transform (DCT) compression standards. In order to reduce blocking artifacts, two image postprocessing techniques, DNLK filter and OCDNLK filter, are presented in this paper. A more accurate DCT domain Kuan’s filter based on Non-local parameter estimation was proposed from the linear minimum mean-square-error (MMSE) criterion. We analyze the required two assumptions for the filter theoretically. Then the DCT domain Kuan’s filter for low frequency coefficients and Non-local mean filter for high frequency AC coefficients constitute the proposed Non-local Kuan’s (NLK) filter. After that, we propose the Dual Non-local Kuan’s (DNLK) filter by applying the proposed filter in dual layer. The DNLK filter is extended to form the Overcomplete Dual Non-local Kuan’s (OCDNLK) filter by applying to the overcomplete DCT coefficients. Experimental results on coded images using test quantization tables and JPEG coded images show the effectiveness of the two methods.