scholarly journals Statistically Matched Wavelet Based Texture Synthesis in a Compressive Sensing Framework

2014 ◽  
Vol 2014 ◽  
pp. 1-18
Author(s):  
Mithilesh Kumar Jha ◽  
Brejesh Lall ◽  
Sumantra Dutta Roy
Keyword(s):  
Image Compression ◽  
Compressive Sensing ◽  
Image Coding ◽  
Texture Synthesis ◽  
Sampling Rate ◽  
Data Representation ◽  
Compression Scheme ◽  
Full Resolution ◽  
Reconstruction Quality ◽  
Frame Work

This paper proposes a statistically matched wavelet based textured image coding scheme for efficient representation of texture data in a compressive sensing (CS) frame work. Statistically matched wavelet based data representation causes most of the captured energy to be concentrated in the approximation subspace, while very little information remains in the detail subspace. We encode not the full-resolution statistically matched wavelet subband coefficients but only the approximation subband coefficients (LL) using standard image compression scheme like JPEG2000. The detail subband coefficients, that is, HL, LH, and HH, are jointly encoded in a compressive sensing framework. Compressive sensing technique has proved that it is possible to achieve a sampling rate lower than the Nyquist rate with acceptable reconstruction quality. The experimental results demonstrate that the proposed scheme can provide better PSNR and MOS with a similar compression ratio than the conventional DWT-based image compression schemes in a CS framework and other wavelet based texture synthesis schemes like HMT-3S.

scholarly journals DEMD-Based Image Compression Scheme in a Compressive Sensing Framework

2014 ◽  
Vol 9 (1) ◽  
pp. 64-78
Author(s):  
Mithilesh Kumar JHA ◽  
Brejesh Lall ◽  
Sumantra Roy
Keyword(s):  
Image Compression ◽  
Compressive Sensing ◽  
Compression Scheme

scholarly journals Optimized Architecture Using a Novel Subexpression Elimination on Loeffler Algorithm for DCT-Based Image Compression

VLSI Design ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Maher Jridi ◽  
Ayman Alfalou ◽  
Pramod Kumar Meher
Keyword(s):  
Image Compression ◽  
High Speed ◽  
Finite Impulse Response ◽  
Data Representation ◽  
Power Reduction ◽  
Compression Scheme ◽  
Silicon Area ◽  
Canonical Signed Digit ◽  
Speed Performance ◽  
Constant Coefficients

The canonical signed digit (CSD) representation of constant coefficients is a unique signed data representation containing the fewest number of nonzero bits. Consequently, for constant multipliers, the number of additions and subtractions is minimized by CSD representation of constant coefficients. This technique is mainly used for finite impulse response (FIR) filter by reducing the number of partial products. In this paper, we use CSD with a novel common subexpression elimination (CSE) scheme on the optimal Loeffler algorithm for the computation of discrete cosine transform (DCT). To meet the challenges of low-power and high-speed processing, we present an optimized image compression scheme based on two-dimensional DCT. Finally, a novel and a simple reconfigurable quantization method combined with DCT computation is presented to effectively save the computational complexity. We present here a new DCT architecture based on the proposed technique. From the experimental results obtained from the FPGA prototype we find that the proposed design has several advantages in terms of power reduction, speed performance, and saving of silicon area along with PSNR improvement over the existing designs as well as the Xilinx core.

scholarly journals Energy and Quality Evaluation for Compressive Sensing of Fetal Electrocardiogram Signals

Sensors ◽  
2016 ◽  
Vol 17 (1) ◽  
pp. 9 ◽  
Author(s):  
Giulia Da Poian ◽  
Denis Brandalise ◽  
Riccardo Bernardini ◽  
Roberto Rinaldo
Keyword(s):  
Energy Consumption ◽  
Compressive Sensing ◽  
Low Complexity ◽  
Heart Beat ◽  
Signal Acquisition ◽  
Compression Scheme ◽  
Fetal Electrocardiogram ◽  
Reconstruction Quality ◽  
Beat Detection ◽  
Reduction Of Energy Consumption

This manuscript addresses the problem of non-invasive fetal Electrocardiogram (ECG) signal acquisition with low power/low complexity sensors. A sensor architecture using the Compressive Sensing (CS) paradigm is compared to a standard compression scheme using wavelets in terms of energy consumption vs. reconstruction quality, and, more importantly, vs. performance of fetal heart beat detection in the reconstructed signals. We show in this paper that a CS scheme based on reconstruction with an over-complete dictionary has similar reconstruction quality to one based on wavelet compression. We also consider, as a more important figure of merit, the accuracy of fetal beat detection after reconstruction as a function of the sensor power consumption. Experimental results with an actual implementation in a commercial device show that CS allows significant reduction of energy consumption in the sensor node, and that the detection performance is comparable to that obtained from original signals for compression ratios up to about 75%.

scholarly journals High Quality Color Image Compression for Discrete Transform Domain Downward Conversion Block Based Image Coding

2019 ◽  
Vol 8 (4) ◽  
pp. 1927-1932
Keyword(s):  
Image Compression ◽  
Image Coding ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Image Data ◽  
Computer Applications ◽  
Compression Scheme ◽  
Text Data ◽  
Block Partition ◽  
Block Based

Text and image data are important elements for information processing almost in all the computer applications. Uncompressed image or text data require high transmission bandwidth and significant storage capacity. Designing and compression scheme is more critical with the recent growth of computer applications. Among the various spatial domain image compression techniques, multi-level Block partition Coding (MLBTC) is one of the best methods which has the least computational complexity. The parameters such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are measured and it is found that the implemented methods of BTC are superior to the traditional BTC. This paves the way for a nearly error free and compressed transmission of the images through the communication channel.

An Algorithm of Image Coding Based on Compressive Sensing

2013 ◽  
Vol 427-429 ◽  
pp. 1849-1852
Author(s):  
Dong Cheng Shi ◽  
Yi Dan Xing ◽  
Xiao Ding Shi
Keyword(s):  
Image Reconstruction ◽  
Compressive Sensing ◽  
Image Coding ◽  
Sampling Rate ◽  
Measurement Matrix ◽  
Block Algorithm ◽  
Optimum Parameters ◽  
Block Sizes ◽  
Reconstruction Model ◽  
Reconstruction Time

Block Compressive Sensing (BCS) is a image reconstruction model based on CS theory. By use the same measurement matrix to obtain the data in the form of Block × Block. Algorithm meaning to solve the problem that the traditional CS measurement matrix required for large storage, but different block has important influence on reconstruction time and effect. In this paper, find out the optimum parameters of the block. By compared the PSNR and reconstructed image effect under different sampling rate and different block sizes.

scholarly journals Fast Transforms in Image Processing: Compression, Restoration, and Resampling

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Leonid P. Yaroslavsky
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Image Restoration ◽  
Compressive Sensing ◽  
Adaptive Filters ◽  
Processing Methods ◽  
Fast Transforms ◽  
Image Transforms

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.

FPGA implementation of a near computation free image compression scheme based on adaptive decimation

Integration ◽  
2003 ◽  
Vol 36 (3) ◽  
pp. 121-143 ◽  
Author(s):  
Angus Wu ◽  
P.W.M. Tsang ◽  
Johnson Tang
Keyword(s):  
Image Compression ◽  
Fpga Implementation ◽  
Compression Scheme
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