Adaptive image compression technique for wireless sensor networks

When using wireless sensor networks for real-time image transmission, some critical points should be considered. These points are limited computational power, storage capability, narrow bandwidth and required energy. Therefore, efficient compression and transmission of images in wireless sensor network is considered. To address the above mentioned concerns, an efficient adaptive compression scheme that ensures a significant computational and energy reduction as well as communication with minimal degradation of the image quality is proposed. This scheme is based on wavelet image transform and distributed image compression by sharing the processing of tasks to extend the overall lifetime of the network. Simulation results are presented and they show that the proposed scheme optimizes the network lifetime, reduces significantly the amount of the required memory and minimizes the computation energy by reducing the number of arithmetic operations and memory accesses.     

Pattern matching image compression: algorithmic and empiricalresults

We propose a non-transform image compression scheme based on approximate 1D pattern matching, called pattern matching image compression (PMIC). The main idea behind it is a lossy extension of the Lempel-Ziv data compression scheme in which one searches for the longest prefix of an uncompressed image that approximately occurs in the already processed image. This main algorithm is enhanced with several new features such as searching for reverse approximate matching, recognizing sub-strings in images that are additively shifted versions of each other, introducing a variable and adaptive maximum distortion level D, and so forth. These enhancements are crucial to the overall quality of our scheme and their efficient implementation leads to algorithmic issues of interest in their own right. Both algorithmic and experimental results are presented. Our scheme turns out to be competitive with JPEG and wavelet compression for good quality graphical images. We also review related theoretical results     

视频局部特征描述子的紧凑表示方法

目的 随着手持移动设备的迅猛发展和大数据时代的到来,以多媒体数据为核心的视觉搜索等研究和应用得到了广泛关注。其中局部特征描述子的压缩、存储和传输起到了举足轻重的作用。为此在传统图像/视频压缩框架中,提出一种高效的视觉局部特征的紧凑表示方法,使得传统内容编码可以适应广泛的检索分析等需求。方法 为了得到紧凑、有区分度、同时高效的局部特征表示,首先引入了多参考的预测机制,在消除了时空冗余的同时,通过充分利用视频纹理编码的信息,消除了来自纹理-特征之间的冗余。此外,还提出了一种新的率失真优化方法——码率-准确率最优化方法,使得基于匹配/检索应用的性能达到最优。结果 在不同数据集上进行验证实验,和最新的视频局部描述子压缩框架进行比较,本文方法能够在保证匹配和检索性能的基础上,显著地减少特征带来的比特消耗,达到大约150:1的压缩比。结论 本文方法适用于传统图像/视频编码框架,通过在码流中嵌入少量表示特征的信息,即可实现高效的检索性能,是一种面向检索等智能设备应用的新型多媒体内容编码框架。     

精度可配置DCT及其VLSI设计

利用离散余弦变换(DCT)能量分布的特性,提出一种精度可配置的DCT及其VLSI结构.根据不同的变换精度需求,通过选择DCT基向量局部最优的分布式算法展开精度,实现了变换精度损失与功耗减少的优化处理;同时对DCT基向量采用正则有符号数(canonical signed digit,CSD)编码,减少了整体电路的硬件资源开销.模拟和综合后的结果表明,该结构适合图像视频等要求低功耗、实时处理领域的可配置性应用.     

基于整数小波变换的图像压缩算法

IWT（integer wavelet transform）是一种基于提升格式整数小波变换的图像压缩算法.IWT比传统的浮点小波变换效率要高.在硬件实现时,整数运算比浮点运算便宜,基于提升格式的整数分解时所需的存储空间只是传统变换的一半.基于此考虑有损压缩,图像先作基于提升格式的整数分解,然后结合改进的EZW和自适应量程编码.在编码性能不受影响的情况下,此算法比Shaprio的EZW编码要快1.5倍.     

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。     

A progressive transmission capable diagnostically lossless compression scheme for 3D medical image sets

This paper presents a novel and efficient diagnostically lossless compression for 3D medical image sets. This compression scheme provides the 3D medical image sets with a progressive transmission capability. An automated filter-and-threshold based preprocessing technique is used to remove noise outside the diagnostic region. Then a wavelet decomposition feature vector based approach is applied to determine the reference image for the entire 3D medical image set. The selected reference image contains the most discernible anatomical structures within a relative large diagnostic region. It is progressively encoded by a lossless embedded zerotree wavelet method so the validity of an entire set can be determined early. This preprocessing technique is followed by an optimal predictor plus a 1st-level integer wavelet transform to de-correlate the 3D medical image set. Run-length and arithmetic coding are used to further remove coding redundancy. This diagnostically lossless compression method achieves an average compression of 2.1038, 2.4292, and 1.6826 bits per pixel for three types of 3D magnetic resonance image sets. The integrated progressive transmission capability degrades the compression performance by an average of 7.25%, 6.60%, and 4.49% for the above three types. Moreover, our compression without and with progressive transmission achieves better compression than the state-of-the-art.     

Wavelet Packet Compression of Medical Images

Tolba, A. S., Wavelet Packet Compression of Medical Images, Digital Signal Processing12 (2002) 441–470The increasing need for efficient image storage and transmission in hospitals imposes heavy requirements on the design of picture archiving and communication systems. Thus new methods are needed for efficient image compression. Recent reviews of wavelets in biomedical applications showed that wavelets should be used with caution and that a particular solution should always be motivated by the problem itself. This study discovers the best design parameters for a data compression scheme applied to medical images of different imaging modalities. The proposed technique aims at reducing the transmission cost while preserving the diagnostic integrity. By selecting the wavelet packet's filters, decomposition level and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimum distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of medical images. Statistical significance tests were performed on the experimental measures to conduct the most suitable wavelet shape for each imaging modality. Image quality measures are used to evaluate the performance of different wavelet filters for different imaging modalities. Image resolution is found to have a remarkable effect on the compression rate.     

基于数据结构化的三维动画压缩方法研究

在三维动画应用日益广泛的背景下,研究三维动画数据压缩方法对其数据的存储、传播和渲染技术至关重要.基于图像数据的结构化特征,计算机视觉领域提出了一系列高效图像压缩技术.鉴于三维动画数据与图像数据结构的差异,探讨三维动画数据结构化方法,进而探索改进图像算法以实现一种新的三维动画数据压缩方法.具体而言,首先研究对三维模型顶点...     

MPEG-4分层视频的自适应传输构架

在结合DirectShow RTP构架和MPEG-4相关数据的RTP负载格式的基础上，提出了一种适合MPEG-4分层视频的自适应多播传输构架。该构架可以根据网络和本机的资源利用状况及用户策略，通过分级控制机制来动态选择传输方案，动态改变各种压缩和传输参数以及协调各个具有不同优先权的视频流，从而在适应资源状况的同时，获得最佳的视频质量和最优的网络利用率。     

异构环境下层次编码多视频源多共享信道分层组播

视频组播是许多当前和将来网络服务的重要组成部分,如视频会议,远程学习、远地展示及视频点播,随着网络传送基础设施的改善和端系统处理能力的增强,组播视频应用日益变得可行,组播视频传输中存在的主要问题是网络送资源的异构性和动态性,其使得视频流的多个接收方都达到可接受的流量特性变得异常困难,目前该问题的一个有效解决方式就是利用自适应的分层视频传输机制,在该机制中,各源产生层次媒体流,并在多个网络信道中传输。对视频会议类的多点到多点视频组播应用,信道往往被所有潜在的发送方共享,任何发送方都可在任何一个共享信道中发送其视频层次。在该多点到多点、共享信道、分层视频组播模型下,一个关键问题就是如何动态确定各视频源层次到各共享组播信道的映射,映射策略直接影响到会话整体视频接收质量和网络带宽利用率。典型的方式是顺序映射,该映射方式同等对待各发送方,但利用该方式,随源数目的增加,在各共享网络信度上会出现带宽可伸缩性问题,而且顺序映射方式无法适应网络传送资源和会话状态的动态变化。为此,该文设计了一种基于接收方反馈信息的自适应的层次映射算法,接收方周期性地将其当前感兴趣的发送方及接收速率的信息反馈给某控制节点,而控制节点就利用当前反馈信息动态地调整映射策略。经证实,该算法始终能比顺序层次映射算法获得更高的整体视频接收质量,并具有高的带宽利用率和很小的复杂度。     

An Adaptive Two-Stage Edge Detection Scheme for Digital Color Images

An adaptive two-stage edge detection scheme for digital color images is proposed in this paper. In the first stage of this scheme, each three-dimensional color image is reduced to a one-dimensional gray-level image using the moment-preserving thresholding technique. Then, a new edge detection technique based on the block truncation coding scheme is introduced to detect the edge boundary in the second stage. The edge detection process makes use of the bit plane information of each BTC-encoded block to detect the edge boundary. The experimental results show that the performance of the detected edge image of the proposed scheme is as good as in Yang's scheme and in the Sobel operator. However, the computational cost consumed by the proposed scheme is less than that of Yang's scheme. In addition, the proposed scheme provides an adaptive edge quality decision mechanism. This mechanism can provide different edge images to meet various applications and the subjective evaluation. Moreover, this scheme locates the edge boundaries to the sub-pixel accuracy, which is an advantage to applications such as data hiding and image watermarking.     

A remote display QoE improvement scheme for interactive applications in low network bandwidth environment

Screen transmission is an essential part of Desktop as a Service (DaaS) which directly influence the quality of experience (QoE). In this paper, we propose a novel QoE improvement scheme that dynamically controls the quality setting of the image compression before the screen transmission to decrease response time of the system still maintaining the satisfactory image quality, hence improves the QoE in interactive applications in a band-limited environment. The proposed scheme first selects the best quality setting appropriate for current network bandwidth quota, then uses the remaining bandwidth to improve the quality setting of low motion regions without any adverse effect on response time. To enable the adaptive quality selection and image quality refinement, we propose a compressed image file size inference model and a block priority calculation method respectively. Particularly, we implement our QoE Improvement Scheme to work with screen content coding. Both quantitative measurements and users’ evaluations in the experiments show that our QoE improvement scheme improves QoS as well as QoE by utilizing the available network bandwidth efficiently.     

一种自适应小波图像压缩算法

本文描述了一种适用于图像压缩的自适应小波方法,该方法采用统一提升框架,并可根据图像局部区域特征,自适应地采用不同的小波滤波器进行处理。该方法已经过计算机模拟并获得了比较好的效果。     

Unequal error protection scheme for image transmission based on regularized variable-node and expanding window fountain codes

A transmission scheme based on rateless codes is proposed for image delivery over additive white Gaussian noise (AWGN) channels. Unequal Error Protection (UEP) characteristics are also introduced to provide prioritized delivery for different layers in content-based image. Firstly, layer segmentation processing based on an improved guided filtering is introduced to decompose the source image into the base layer and detail layer. Then, a windowing technique is applied to produce a bias towards certain classes of layers according to their various protection requirements. Variable-node degree distribution is also exploited to provide enhanced UEP and improve the high error floor suffered by some conventional UEP schemes. Compared with the conventional image transmission schemes based on rateless codes with UEP, the additional parameters introduced by the proposed scheme make it more general and flexible for individual applications. Furthermore, the proposed approach can provide better UEP performance and improve the network image transmission quality, which are confirmed both theoretically and experimentally.

     

机载遥感系统超光谱图像分层近无损压缩

针对机载遥感系统超光谱图像实时压缩与传输的需求,分析了超光谱图像位平面的相关特性并设计了超光谱图像分层近无损压缩方案。高位采用防误码的DPCM去相关,依据图像复杂度实时调节压缩比以适应传输带宽;低位按一致性准则进行四叉树划分并取均值,均匀量化残差图像后进行编码,得到混合码流。算法简单有效,实际压缩比和预设压缩比差别不超过5%。压缩比接近10时,复原图像PSNR值大于33,高位位平面平均相似度不低于0.98。     

Best parameters selection for wavelet packet-based compression of magnetic resonance images.

Transmission of compressed medical images is becoming a vital tool in telemedicine. Thus new methods are needed for efficient image compression. This study discovers the best design parameters for a data compression scheme applied to digital magnetic resonance (MR) images. The proposed technique aims at reducing the transmission cost while preserving the diagnostic information. By selecting the wavelet packet's filters, decomposition level, and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimal distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of MR images. In all cases, decomposition at three or four levels with the Coiflet 5 wavelet (Coif 5) results in better compression performance than the other wavelets. Image resolution is found to have a remarkable effect on the compression rate.     

Energy-Constrained Distortion Reduction Optimization for Wavelet-Based Coded Image Transmission in Wireless Sensor Networks

Image transmissions in Wireless Multimedia Sensor Networks (WMSNs) are often energy constrained. They also have requirement on distortion minimization, which may be achieved through Unequal Error Protection (UEP) based communication approaches. In related literature with regard to wireless multimedia transmissions, significantly different importance levels between image-pixel-position information and image-pixel-value information have not been fully exploited by existing UEP schemes. In this paper, we propose an innovative image-pixel-position information based resource allocation scheme to optimize image transmission quality with strict energy budget constraint for image applications in WMSNs, and it works by exploring these uniquely different importance levels among image data streams. Network resources are optimally allocated cross PHY, MAC and APP layers regarding inter-segment dependency, and energy efficiency is assured while the image transmission quality is optimized. Simulation results have demonstrated the effectiveness of the proposed approach in achieving the optimal image quality and energy efficiency. The performance gain in terms of distortion reduction is especially prominent with strict energy budget constraints and lower image compression ratios.      

A Customized Framework to Recompress Massive Internet Images

Recently,device storage capacity and transmission bandwidth requirements are facing a heavy burden on account of massive internet images.Generally,to improve user experience and save costs as much as possible,a lot of internet applications always focus on how to achieve appropriate image recompression.In this paper,we propose a novel framework to efficiently customize image recompression according to a variety of applications.First of all,we evaluate the input image’s compression level and predict an initial compression level which is very close to the final output of our system using a prior learnt from massive images.Then,we iteratively recompress the input image to different levels and measure the perceptual similarity between the input image and the new result by a block-based coding quality method.According to the output of the quality assessment method,we can update the target compression level,or switch to the subjective evaluation,or return the final recompression result in our system pipeline control.We organize subjective evaluations based on different applications and obtain corresponding assessment report.At last,based on the assessment report,we set up a series of appropriate parameters for customizing image recompression.Moreover,our new framework has been successfully applied to many commercial applications,such as web portals,e-commerce,online game,and so on.     

基于频域压缩和二维离散混沌的图像加密方案

针对数字图像加密安全与效率的矛盾性问题,提出了一个基于二维离散超混沌和频域压缩的数字图像加密方案。首先,通过对图像分块得到图像矩阵,并将其转换成频域矩阵。然后在密钥的驱动下,使用超混沌系统对频域矩阵进行加密,使其具有很高的安全性和随机性。接下来,采用哈达玛变换对加密后的频域矩阵进行变换,进一步增强加密的安全性。最后,采用压缩编码技术对加密后的矩阵进行压缩,达到减小传输数据量的目的。实验结果表明,提出的图像加密方案具有很高的加密强度和安全性,在保证加密效率和图像质量的前提下,减小了传输数据的大小,提高了传输效率。同时,该方案具有较强的鲁棒性,能够抵御各种攻击,具有良好的应用前景。