scholarly journals An Efficient Image Compressor for Charge Coupled Devices Camera

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Jin Li ◽  
Fei Xing ◽  
Zheng You
Keyword(s):  
Remote Sensing ◽  
Wavelet Transforms ◽  
Ccd Camera ◽  
Low Complexity ◽  
Remote Sensing Image ◽  
Discrete Wavelet ◽  
Bit Rate ◽  
Compression Scheme ◽  
Low Bit Rate ◽  
Charge Coupled Devices

Recently, the discrete wavelet transforms- (DWT-) based compressor, such as JPEG2000 and CCSDS-IDC, is widely seen as the state of the art compression scheme for charge coupled devices (CCD) camera. However, CCD images project on the DWT basis to produce a large number of large amplitude high-frequency coefficients because these images have a large number of complex texture and contour information, which are disadvantage for the later coding. In this paper, we proposed a low-complexity posttransform coupled with compressing sensing (PT-CS) compression approach for remote sensing image. First, the DWT is applied to the remote sensing image. Then, a pair base posttransform is applied to the DWT coefficients. The pair base are DCT base and Hadamard base, which can be used on the high and low bit-rate, respectively. The best posttransform is selected by thelp-norm-based approach. The posttransform is considered as the sparse representation stage of CS. The posttransform coefficients are resampled by sensing measurement matrix. Experimental results on on-board CCD camera images show that the proposed approach significantly outperforms the CCSDS-IDC-based coder, and its performance is comparable to that of the JPEG2000 at low bit rate and it does not have the high excessive implementation complexity of JPEG2000.

Advanced Information Hiding for G.711 Telephone Speech

2013 ◽  
pp. 129-163 ◽  
Author(s):  
Akinori Ito ◽  
Yôiti Suzuki
Keyword(s):  
Data Hiding ◽  
Side Information ◽  
Low Complexity ◽  
Speech Quality ◽  
Rate Data ◽  
Bit Rate ◽  
Bandwidth Extension ◽  
Low Bit Rate ◽  
Speech Codec ◽  
Popular Speech

G.711 is the most popular speech codec for Voice over IP (VoIP). This chapter proposes a method for embedding data into G.711-coded speech for conveying side information for enhancing speech quality such as bandwidth extension or packet loss concealment. The proposed method refers to a low-bit rate encoder to determine how many bits are embedded into each sample. First, a variable-bit rate data hiding method is proposed as a basic framework of the proposed method. Then, the proposed method is extended to achieve fixed bit rate data hiding. According to comparison experiments, the proposed method is proved to achieve higher speech quality compared with the conventional method. Moreover, the authors developed a low-complexity speech bandwidth extension method that uses the proposed data hiding method.

Very Low Bit-rate Video Coding

2011 ◽  
pp. 100-148
Author(s):  
Manoranjan Paul ◽  
Manzur Murshed ◽  
Laurence S. Dooley
Keyword(s):  
Video Coding ◽  
Vector Quantization ◽  
Parametric Model ◽  
Future Research ◽  
The Internet ◽  
Discrete Wavelet ◽  
Video Communications ◽  
Bit Rate ◽  
Low Bit Rate ◽  
Shaped Pattern

his chapter presents a contemporary review of the various different strategies available to facilitate Very Low Bit-Rate (VLBR) coding for video communications over mobile and fixed transmission channels as well as the Internet. VLBR media is typically classified as having a bit rate between 8 and 64 Kbps. Techniques that are analyzed include Vector Quantization, various parametric model-based representations, the Discrete Wavelet and Cosine Transforms, and fixed and arbitrary shaped pattern-based coding. In addition to discussing the underlying theoretical principles and relevant features of each approach, the chapter also examines their benefits and disadvantages, together with some of the major challenges that remain to be solved. The chapter concludes by providing some judgments on the likely focus of future research in the VLBR coding field.

scholarly journals Change Detection Algorithm Based on Discrete Wavelet Transforms and Neighborhood Fuzzy C-Means

2018 ◽  
Vol 36 (3) ◽  
pp. 426-431
Author(s):  
Fengping Wang ◽  
Weixing Wang ◽  
Ting Gao ◽  
Weiwei Chen ◽  
Hongxia Li
Keyword(s):  
Remote Sensing ◽  
Wavelet Transforms ◽  
Detection Probability ◽  
Detection Algorithm ◽  
Spatial Distance ◽  
Ratio Method ◽  
Discrete Wavelet ◽  
Forest Change ◽  
Distance Information ◽  
Ratio Image

A new algorithm on Discrete Wavelet Transform (DWT) and neighborhood FCM is proposed to detect change area from remote sensing image. First, the subtraction and ratio image are obtained by the subtraction and ratio method from the two registered remote sensing images; Then, the DWT is applied to the subtraction and ratio image, the region intensity-based and energy-based fusion rules is adopted to the low frequency and high frequency wavelet coefficients, and the inverse DWT is used to obtain the final difference image; At last, the neighborhood FCM is carried out to get the change areas, the spatial distance information and gray difference information are considered in the objective function of FCM, which could avoid misclassification and enhance the detection probability. Experimental results show that the proposed algorithm has strong ability to suppress noise and good detection results; the detection probability of unban change area can reach to 98.45%, whereas, the detection probability is up to 87.5% for the discontinuous forest change area.

scholarly journals Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
K. Parvathi ◽  
B. S. Prakasa Rao ◽  
M. Mariya Das ◽  
T. V. Rao
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
High Resolution ◽  
Wavelet Transforms ◽  
Intensity Variation ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Watershed Segmentation ◽  
Remote Sensing Images ◽  
Grey Scale

The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal) and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

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