Enabling energy-efficient and lossy-aware data compression in wireless sensor networks by multi-objective evolutionary optimization

Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objective evolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity.     

Huffman算法的改进及其在图像压缩中的应用

本文改进了Huffman编码算法,主要是针对Huffman编码生成Huffman树构造中的排序方法的改进,提出一种基于"堆排序"的新方法。采用堆排序找到最小值实现Huffman编码,经过这种改进的Huffman编码方法对内存读写的次数大为减少,从而提高了响应速度。使得Huffman编码效率有所提高。通过对JPEG的Huffman压缩算法的分析以及采用4个JPG文件对改进的和传统的Huffman算法进行了仿真实验,对比分析表明改进算法的性能无论是压缩比率还是压缩时间方面都比经典的Huffman算法性能有所提高。     

数据分析及无损编码技术在电脑袜机上的应用

该文主要介绍了袜机数据的整合及对整合的效率进行了分析,有效的减小了数据所占用的位数;根据袜机数据的特点且存在大量的重复性字符,我们应用无损编码技术对袜机数据冗余部分进行压缩,通过比较几种无损编码技术的平均压缩率,得出了最适合袜机数据的压缩方式。     

Lapped transform‐based codec for frame‐memory reduction in super‐quality LCD overdrive

Abstract— Overdrive is commonly used to reduce the liquid‐crystal response time and motion blur in liquid‐crystal displays (LCDs). However, overdrive requires a large frame memory in order to store the previous frame for reference. In this paper, a lapped transform‐based codec (LTC) is proposed to reduce the frame memory needed for LCD overdrive. In the latest literature, a directional prediction‐based codec (DPC) employs eight directional predictions, which takes up a large percentage of the computational complexity of the codec and does not consider the de‐correlation of the inter‐blocks. Therefore, the LTC first uses the lapped transform to decompose the correlation of the inter‐blocks in the YUV color space. A hadamard transform is then used for energy compaction. The reordered coefficients are pre‐quantized and encoded using the proposed adaptive bit‐plane coding (ABPC) method for a simple hardware implementation. The simulation results show that the proposed LTC significantly improves the DPC in both subjective and objective performance and outperforms the block truncation coding (BTC) and adaptive multi‐level BTC (AM‐BTC), which have been described in the literature.     

Visual‐quality estimation using objective metrics

Abstract— In the past few years, a big effort in the scientific community has been devoted to the development of better image‐ and video‐quality metrics that correlate well with the human perception of quality. In this paper, an overview of the main ideas used in the design of objective quality metrics is given. More specifically, we briefly describe the different types of objective metrics and present a representative set of the different approaches taken by these algorithms. Finally, the challenges and recent developments in the area of image and video quality are discussed.     

Post BWT stages of the Burrows–Wheeler compression algorithm

The lossless Burrows–Wheeler compression algorithm has received considerable attention over recent years for both its simplicity and effectiveness. It is based on a permutation of the input sequence—the Burrows–Wheeler transformation (BWT)—which groups symbols with a similar context close together. In the original version, this permutation was followed by a Move‐To‐Front transformation and a final entropy coding stage. Later versions used different algorithms, placed after the BWT, since the following stages have a significant influence on the compression rate. This paper describes different algorithms and improvements for these post BWT stages including a new context‐based approach. The results for compression rates are presented together with compression and decompression times on the Calgary corpus, the Canterbury corpus, the large Canterbury corpus and the Lukas 2D 16‐bit medical image corpus. Copyright © 2010 John Wiley & Sons, Ltd.     

基于矩形NAM和偏微分方程的灰度图像压缩方法

借助矩形NAM图像表示方法和偏微分方程技术,提出一种新的灰度图像压缩方法.该方法在编码端把原始图像逆布局为若干矩形子块,并采用坐标压缩方法存储每个子块的位置、大小.在解码端,采用偏微分方程进行图像修复,有效消除解码图像中的方块效应.该方法的时间复杂度为 ,其中n为灰度图像像素数.实验表明:在保持图像质量的前提下,该方法具有更高的压缩比和更少的块数,具有较高实用价值.     

SPC: Fast and Efficient Scalable Predictive Coding of Animated Meshes

Animated meshes are often represented by a sequence of static meshes with constant connectivity. Due to their frame-based representation they usually occupy a vast amount of bandwidth or disk space. We present a fast and efficient scalable predictive coding (SPC) scheme for frame-based representations of animated meshes. SPC decomposes animated meshes in spatial and temporal layers which are efficiently encoded in one pass through the animation. Coding is performed in a streamable and scalable fashion. Dependencies between neighbouring spatial and temporal layers are predictively exploited using the already encoded spatio-temporal neighbourhood. Prediction is performed in the space of rotation-invariant coordinates compensating local rigid motion. SPC supports spatial and temporal scalability, and it enables efficient compression as well as fast encoding and decoding. Parts of SPC were adopted in the MPEG-4 FAMC standard. However, SPC significantly outperforms the streaming mode of FAMC with coding gains of over 33%, while in comparison to the scalable FAMC, SPC achieves coding gains of up to 15%. SPC has the additional advantage over FAMC of achieving real-time encoding and decoding rates while having only low memory requirements. Compared to some other non-scalable state-of-the-art approaches, SPC shows superior compression performance with gains of over 16% in bit-rate.     

Real time fractal image coder based on characteristic vector matching

Fractal coding algorithm has many applications including image compression. In this paper a classification scheme is presented which allows the hardware implementation of the fractal coder. High speed and low power consumption are the goal of the suggested design. The introduced method is based on binary classification of domain and range blocks. The proposed technique increases the processing speed and reduces the power consumption while the qualities of the reconstructed images are comparable with those of the available software techniques. In order to show the functionality of the proposed algorithm, the architecture was implemented on a FPGA chip. The application of the proposed hardware is shown in image compression. The resulted compression ratios, PSNR error, gate count, compression speed and power consumption are compared with the existing designs. Other applications of the proposed design are feasible in certain fields such as mass–volume database coding and also in video coder’s block matching schemes.     

改进的嵌入式小波零树编码算法

在分析EZW算法的基础上,提出一种改进的图像编码算法,把小波系数分解成2组较小的小波系数,分别对其进行编码,并移除EZW算法中的副扫描过程,从而省去了存储重要系数的位置信息的比特数,减少了EZW算法中扫描次数,在很大程度上提高编码速度,缩减需要编码的符号数目,是种快速的编码算法。实验结果表明,运用该算法处理得到的图像质量能够满足大多数的应用需求。