Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION
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This document presents a VLSI architecture for an image compression system using vector quantization. It discusses motivation, objectives, and provides an introduction to image compression techniques including scalar and vector quantization. It describes the LBG algorithm for generating codebooks and proposes a multistage vector quantization approach. A cost-effective VLSI architecture for multistage vector quantization is presented, including the design of individual components like the image processing unit and decompression control unit. Results show the proposed architecture can compress images with good quality while maintaining low hardware complexity.
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Computer Organozation
Pipeline and Vector Processing(Parallel Processing Pipeline,Arithmetic Pipeline,Instruction Pipeline,RISC Pipeline,Vector Processing, Array Processors) and Types of Memory Organization
M.Tech project on Haar wavelet based approach for image compression
This document presents an image compression technique using Haar wavelet transforms. It begins with background on the need for image compression due to increasing image sizes. It then outlines the encoder and decoder block diagrams and classifications of compression techniques. The key aspects of the proposed technique are presented: using wavelet transforms to exploit spatial and frequency correlations, applying averaging and differencing operations on rows and columns, thresholding the transformed matrix, and reconstructing the original image. Advantages include conceptual simplicity, speed, memory efficiency and reversibility. Results and future work are discussed before concluding on the benefits of wavelet-based compression.
Coa swetappt copy
Vector processing involves performing arithmetic operations on large arrays of numbers represented as vectors and matrices. There are two main types of array processors - attached array processors which are auxiliary processors that improve a general purpose computer's performance on numerical tasks, and SIMD array processors which have multiple synchronized processing units operating in parallel on a single instruction stream with multiple data. SIMD array processors contain processing elements with local memory that perform the same operation simultaneously under a master control unit.
vector QUANTIZATION
Vector quantization maps high-dimensional vectors to codewords from a finite codebook. Each codeword defines a Voronoi region containing vectors closest to that codeword. The Lloyd and LBG algorithms are commonly used to optimize the codebook for a given dataset by iteratively clustering vectors and recomputing codeword averages. Tree-structured vector quantization improves efficiency by recursively partitioning the codebook into binary groups defined by test vectors. This reduces the number of distance comparisons needed at the cost of potential increases in distortion and storage requirements.
Data Compression In SQL
Data compression in SQL can reduce database storage and lead to fewer I/O operations by compressing data at the row and page level. Row compression reclaims unused space from data types like integers and datetimes by storing them in fewer bytes. Page compression additionally uses techniques like prefix and dictionary compression, where common patterns are detected and replaced with references to reduce redundancy. While compression provides storage benefits, it can also increase CPU usage and only works for certain data types. Administrators must consider their specific workload and database objects when deciding what to compress.
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This document discusses multivector and SIMD computers. It covers vector processing principles including vector instruction types like vector-vector, vector-scalar, and vector-memory instructions. It also discusses compound vector operations, vector loops and chaining. Finally, it discusses SIMD computer implementation models like distributed and shared memory, and SIMD instruction types.
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Survey paper on image compression techniques
This document summarizes and compares several popular image compression techniques: wavelet compression, JPEG/DCT compression, vector quantization (VQ), fractal compression, and genetic algorithm compression. It finds that all techniques perform satisfactorily at 0.5 bits per pixel, but for very low bit rates like 0.25 bpp, wavelet compression techniques like EZW perform best in terms of compression ratio and quality. Specifically, EZW and JPEG are more practical than others at low bit rates. The document also notes advantages and disadvantages of each technique and concludes hybrid approaches may achieve even higher compression ratios while maintaining image quality.
Clustered Compressive Sensingbased Image Denoising Using Bayesian Framework
This paper provides a compressive sensing (CS) method of denoising images using Bayesian
framework. Some images, for example like magnetic resonance images (MRI) are usually very
weak due to the presence of noise and due to the weak nature of the signal itself. So denoising
boosts the true signal strength. Under Bayesian framework, we have used two different priors:
sparsity and clusterdness in an image data as prior information to remove noise. Therefore, it is
named as clustered compressive sensing based denoising (CCSD). After developing the
Bayesian framework, we applied our method on synthetic data, Shepp-logan phantom and
sequences of fMRI images. The results show that applying the CCSD give better results than
using only the conventional compressive sensing (CS) methods in terms of Peak Signal to Noise
Ratio (PSNR) and Mean Square Error (MSE). In addition, we showed that this algorithm could
have some advantages over the state-of-the-art methods like Block-Matching and 3D
Filtering (BM3D).
Clustered compressive sensingbased
This paper provides a compressive sensing (CS) method of denoising images using Bayesian framework. Some images, for example like magnetic resonance images (MRI) are usually very
weak due to the presence of noise and due to the weak nature of the signal itself. So denoising
boosts the true signal strength. Under Bayesian framework, we have used two different priors:
sparsity and clusterdness in an image data as prior information to remove noise. Therefore, it is
named as clustered compressive sensing based denoising (CCSD). After developing the
Bayesian framework, we applied our method on synthetic data, Shepp-logan phantom and
sequences of fMRI images. The results show that applying the CCSD give better results than
using only the conventional compressive sensing (CS) methods in terms of Peak Signal to Noise
Ratio (PSNR) and Mean Square Error (MSE). In addition, we showed that this algorithm could
have some advantages over the state-of-the-art methods like Block-Matching and 3D
Filtering (BM3D).
A systematic image compression in the combination of linear vector quantisati...
A systematic image compression in the combination of linear vector quantisati...eSAT Publishing House
1) The document presents a method for image compression that combines linear vector quantization and discrete wavelet transform.
2) Linear vector quantization is used to generate codebooks and encode image blocks, achieving better PSNR and MSE than self-organizing maps.
3) The encoded blocks are then subjected to discrete wavelet transform. Low-low subbands are stored for reconstruction while other subbands are discarded.
4) Experimental results show the proposed method achieves higher PSNR and lower MSE than existing techniques, preserving both texture and edge information.A1804010105
This document presents a scalable method for image classification using sparse coding and dictionary learning. It proposes parallelizing the computation of image similarity for faster recognition. Specifically, it distributes the task of measuring similarity between images among multiple cores in a cluster. Experimental results on a face recognition dataset show nearly linear speedup when balancing the dataset size and number of nodes. Reconstruction errors are used as a similarity measure, with dictionaries learned using K-SVD for each image. The proposed parallel method distributes this similarity computation process to achieve faster image classification.
EFFICIENT IMAGE COMPRESSION USING LAPLACIAN PYRAMIDAL FILTERS FOR EDGE IMAGES
This project presents a new image compression technique for the coding of retinal and
fingerprint images. Retinal images are used to detect diseases like diabetes or
hypertension. Fingerprint images are used for the security purpose. In this work, the
contourlet transform of the retinal and fingerprint image is taken first. The coefficients of
the contourlet transform are quantized using adaptive multistage vector quantization
scheme. The number of code vectors in the adaptive vector quantization scheme depends
on the dynamic range of the input image.
Hybrid compression based stationary wavelet transforms
This document presents a hybrid compression approach for images that uses Stationary Wavelet Transforms (SWT), Back Propagation Neural Network (BPNN), and Lempel-Ziv-Welch (LZW) compression. The approach involves: 1) preprocessing the image, 2) applying SWT, 3) converting to a 1D vector using zigzag scan, and 4) hybrid compression using BPNN vector quantization and LZW lossless compression. Experimental results show the SWT with BPNN and LZW achieves the highest compression ratios but the longest processing time, while SWT with Run Length encoding has a lower ratio but shorter time. The hybrid approach combines lossy and lossless compression techniques to obtain a
Compressed sensing applications in image processing & communication (1)
This document discusses applications of compressed sensing in image processing and communication. It begins by explaining what compressed sensing is and how it works by acquiring signals using fewer samples than required by the Nyquist criterion through exploiting sparsity and incoherence. It then discusses how to implement compressed sensing using properties of sparsity and incoherence along with convex optimization for reconstruction. Applications discussed include image fusion, representation, denoising, remote sensing, MIMO and cognitive radio communications, and ultra-wideband channel estimation. Compressed sensing is shown to provide benefits like reducing data acquisition and transmission costs while enabling reliable signal reconstruction.
An Image representation using Compressive Sensing and Arithmetic Coding
The demand for graphics and multimedia communication over intenet is growing day by day. Generally the coding efficiency achieved by CS measurements is below the widely used wavelet coding schemes (e.g., JPEG 2000). In the existing wavelet-based CS schemes, DWT is mainly applied for sparse representation and the correlation of DWT coefficients has not been fully exploited yet. To improve the coding efficiency, the statistics of DWT coefficients has been investigated. A novel CS-based image representation scheme has been proposed by considering the intra- and inter-similarity among DWT coefficients. Multi-scale DWT is first applied. The low- and high-frequency subbands of Multi-scale DWT are coded separately due to the fact that scaling coefficients capture most of the image energy. At the decoder side, two different recovery algorithms have been presented to exploit the correlation of scaling and wavelet coefficients well. In essence, the proposed CS-based coding method can be viewed as a hybrid compressed sensing schemes which gives better coding efficiency compared to other CS based coding methods.
Multimodal Medical Image Fusion Based On SVD
Image fusion is a promising process in the field of medical image processing, the idea behind is to
improve the content of medical image by combining two or more multimodal medical images. In this paper a
novel fusion framework based on singular value decomposition - based image fusion algorithm is proposed.
SVD is an image adaptive transform, it transforms the matrix of the given image into product USVT
, which
allows to refactor a digital image into three matrices called tensors. The proposed algorithm picks out
informative image patches of source images to constitute the fused image by processing the divided subtensors
rather than the whole tensor and a novel sigmoid-function-like coefficient-combining scheme is applied to
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approach.
RGB Image Compression using Two-dimensional Discrete Cosine Transform
The document discusses a new image compression scheme that incorporates a model of the human visual system (HVS) to improve both quantitative and qualitative performance over existing techniques like JPEG2000. It presents a new HVS model with two components: contrast sensitivity function (CSF) masking, which weights wavelet coefficients by human sensitivity to spatial frequencies, and asymmetric compression, which more severely quantizes chrominance than luminance. Test results showed the new HVS scheme improved peak signal-to-noise ratio and subjective quality for color images over JPEG2000, and improved subjective quality even with lowered PSNR for grayscale images. The paper proposes several avenues for further improvement.
A Novel Approaches For Chromatic Squander Less Visceral Coding Techniques Usi...
Recent advances in video capturing and display technologies, along with the exponentially increasing demand of
video services, challenge the video coding research community to design new algorithms able to significantly
improve the compression performance of the current H.264/AVC standard. This target is currently gaining
evidence with the standardization activities in the High Efficiency Video Coding (HEVC) project. The distortion
models used in HEVC are mean squared error (MSE) and sum of absolute difference (SAD). However, they are
widely criticized for not correlating well with perceptual image quality. The structural similarity (SSIM) index
has been found to be a good indicator of perceived image quality. Meanwhile, it is computationally simple
compared with other state-of-the-art perceptual quality measures and has a number of desirable mathematical
properties for optimization tasks. We propose a perceptual video coding method to improve upon the current
HEVC based on an SSIM-inspired divisive normalization scheme as an attempt to transform the DCT domain
frame prediction residuals to a perceptually uniform space before encoding.
Based on the residual divisive normalization process, we define a distortion model for mode selection and show
that such a divisive normalization strategy largely simplifies the subsequent perceptual rate-distortion
optimization procedure. We further adjust the divisive normalization factors based on local content of the video
frame. Experiments show that the scheme can achieve significant gain in terms of rate-SSIM performance and
better visual quality when compared with HEVC
A Novel Approaches For Chromatic Squander Less Visceral Coding Techniques Usi...
Recent advances in video capturing and display technologies, along with the exponentially increasing demand of
video services, challenge the video coding research community to design new algorithms able to significantly
improve the compression performance of the current H.264/AVC standard. This target is currently gaining
evidence with the standardization activities in the High Efficiency Video Coding (HEVC) project. The distortion
models used in HEVC are mean squared error (MSE) and sum of absolute difference (SAD). However, they are
widely criticized for not correlating well with perceptual image quality. The structural similarity (SSIM) index
has been found to be a good indicator of perceived image quality. Meanwhile, it is computationally simple
compared with other state-of-the-art perceptual quality measures and has a number of desirable mathematical
properties for optimization tasks. We propose a perceptual video coding method to improve upon the current
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frame prediction residuals to a perceptually uniform space before encoding.
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A0540106
This document presents a novel approach for jointly optimizing spatial prediction and transform coding in video compression. It aims to improve performance and reduce complexity compared to existing techniques. The proposed method uses singular value decomposition (SVD) to compress images. SVD decomposes an image matrix into three matrices, allowing the image to be approximated using only a few singular values. This achieves compression by removing redundant information. The document outlines the SVD approach for image compression and measures compression performance using compression ratio and mean squared error between the original and compressed images. It then discusses trends in image and video coding, including combining natural and synthetic content. Finally, it provides a block diagram of the proposed system and compares its compression performance to existing discrete cosine transform-
40120140507003
The document describes a method for multiresolution image fusion using nonsubsampled contourlet transform (NSCT). NSCT provides multiresolution analysis and captures directional information better than wavelets. The method uses NSCT to learn edges from a high-resolution panchromatic image and fuse it with low-resolution multispectral images. Maximum a posteriori estimation with a Markov random field prior is used to optimize the fused image. Experimental results on fusing QuickBird satellite images show the proposed method produces higher quality fused images compared to other fusion methods.
40120140507003
The document describes a method for multiresolution image fusion using nonsubsampled contourlet transform (NSCT). NSCT provides multiresolution analysis with shift invariance and directionality. The method uses NSCT to learn edges from a high-resolution panchromatic image and fuse it with low-resolution multispectral images. Maximum a posteriori estimation with a Markov random field prior is used to optimize the fused image. Experimental results on fusing QuickBird satellite images show the proposed method produces higher quality fused images compared to other fusion methods.
RTL Implementation of image compression techniques in WSN
The Wireless sensor networks have limitations regarding data redundancy, power and require high bandwidth when used for multimedia data. Image compression methods overcome these problems. Non-negative Matrix Factorization (NMF) method is useful in approximating high dimensional data where the data has non-negative components. Another method of the NMF called (PNMF) Projective Nonnegative Matrix Factorization is used for learning spatially localized visual patterns. Simulation results show the comparison between SVD, NMF, PNMF compression schemes. Compressed images are transmitted from base station to cluster head node and received from ordinary nodes. The station takes on the image restoration. Image quality, compression ratio, signal to noise ratio and energy consumption are the essential metrics measured for compression performance. In this paper, the compression methods are designed using Matlab.The parameters like PSNR, the total node energy consumption are calculated. RTL schematic of NMF SVD, PNMF methods is generated by using Verilog HDL.
Implementation of LSB-Based Image Steganography Method for effectiveness of D...
Increased use of electronic communication has given birth to new ways of transmitting information securely. Steganography is a science of hiding information by embedding it in some other data called host message. Images are most known objects for steganography. The host message before steganography and stego message after steganography have the same characteristics. The given work is to be done by evaluating it on MATALAB. While evaluation one can calculate SNR, PSNR and BER for individual information Bit for conceal bit and analysis effect on results.
An Effect of Compressive Sensing on Image Steganalysis
This document discusses using compressive sensing (CS) to recover secret signals that have been embedded in images through steganography. It proposes applying CS to extract features from stego-images using a block CS measurements matrix in order to detect differences between cover and stego-images. The reconstructed image from CS is then used to recover the embedded secret signal values. The method is compared to existing steganalysis techniques that cannot recover secret signals but can only detect their presence. It is concluded that applying CS in this domain could provide an improved way to detect and recover hidden secret messages from digital images.
T4408103107
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
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Chapter 6
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1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
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Chapter wise All Notes of First year Basic Civil Engineering.pptx
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Project pptVLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION
- 1. VLSI ARCHITECTURE FOR AN IMAGE COMPRESSION SYSTEM USING VECTOR QUANTIZATION Presented by: DEBASISH PAIKARAY PRATYUSH KU. SAHOO SAUMYA RANJAN NANDA ABINASH MISHRA Guided By: Mr. P.K.NANDA Asst. Professor Dept. of ECE
- 2. TALK FLOW Motivation Objective Introduction Image compression techniques Distortion measures Scalar quantization Vector quantization LBG Algorithm MSVQ VLSI Architecture of MSVQ Cost effective VLSI Architecture of MSVQ Results & Analysis Conclusion Reference
- 3. Motivation Better Result can be achieved by Multistage Vector Quantization over Single stage Vector Quantization.
- 4. Objective To propose a VLSI Architecture for an image compression system using Vector Quantization
- 5. Introduction Data compression is a process of reducing the amount of data required to represent a given quantity of information, so that it takes lesser storage space and lesser transmission time than the data which is not compressed. A fundamental goal of data compression is to reduce the bit rate for transmission or data storage while maintaining an acceptable fidelity or image quality.
- 6. Fundamentals • R = 1 – (1/C ); C = b / b’ C =compression ratio •If C = 10 (or 10:1), for larger representation has 10 bits of data for every 1 bit of data in smaller representation. So, R = 0.9, indicating that 90 % of its data is redundant.
- 7. Compression Techniques 2 types of compression techniques: 1) Lossless Compression: Examples : Scalar Quantization 2) Lossy Compression: Examples : JPEG , VQ
- 8. Distortion Measures The size of the error relative to the signal is given by the signal-to-noise ratio (SNR) Another common measure is the peak- signal-to-noise ratio (PSNR) The average pixel difference is given by the Mean Square Error (MSE)
- 9. Scalar quantization y=Q(x) y =Q(x) Q: R C Where R is the real line C={y1, y2,…, yN}
- 10. Vector quantization A generalization of scalar quantization to quantization of a vector Scalar quantization Vector quantization
- 11. Vector Quantization encoding 1-D ANALYSIS:
- 13. 2-D ANALYSIS:
- 14. Important Terminologies 1.Euclidean Space 2.Vornoi Region 3.Code Vector(Each red dot) 4.Code Word(16 red dot) 5.Index
- 15. Diagramatic Representation of compression & decompression using VQ
- 16. VQ procedure
- 18. Finding out the perfect codebook: Distance Calculation:
- 20. Proposed Image Coding Scheme
- 22. Block diagram of three stage Multistage Vector Quantizer
- 23. Different subbands of Image
- 24. VLSI architecture for MSVQ
- 25. Cost-effective VLSI architecture for MSVQ
- 26. VLSI architecture of MDC(IPU & DCU)
- 27. High-performance MDC VLSI architecture
- 28. Result of LBG Algorithm Decompressed Image Original Image Compressed Image
- 29. TBW Diagram of Multiplier
- 30. TBW Diagram of Buffer TBW Diagram of Adder TBW diagram of MUX
- 31. Write mode operation of RAM Read mode operation of RAM
- 32. Application of vector quantization Vector quantization technique is efficiently used in various areas of biometric modalities like finger print pattern recognition ,face recognition by generating codebooks of desired size.
- 33. Conclusion We have successfully designed an efficient codebook using LBG Algorithm & proposed an cost effective MSVQ VLSI architecture for an Image compression system.
- 34. REFERENCES 1.Y. Linde, A. Buzo, and R. M. Gray, “An algorithm for vector quantizer design,” IEEE Trans. Commun., vol. COM-28, pp. 84-95, Jan. 1980. 2.Nasser m. Nasrabadi, Membeire,E E E,A nd robert A. King,” Image Coding Using Vector Quantization: A Review” IEEE Transactions on Communications, vol. 36, no. 8, august 1988 3. A. K. Jain, “Image data compression: A review,” Proc. IEEE, vol. 69, pp. 349-389, Mar. 1981. 4. A. Buzo, A. H. Gray, R. M. Gray, and J. D. Markel, “Speech coding based upon vector quantization,” IEEE Trans. Acoust. Speech, Signal Processing, vol. ASSP-28, pp. 562- 574, Oct. 1980. 5. R. M. Gray, “Vector quantization,” IEEE ASSP Mag., pp. 4-29, Apr. 1984. 6.Khalid Sayood ,”Introduction to Image Compression”,3rd edition 7. Seung-Kwon Paek and Lee-Sup Kim,”A Real Time Wavelet VQ Algorithm and Its VLSI Architecture”, IEEE Transaction on Circuits & Systems for video Technology, Vol. 10, No. 3,April 2000. 8. Tzu-Chuen Lu, Ching-Yun Chang, “A Survey of VQ Codebook Generation” , Journal of Information Hiding and Multimedia Signal Processing, Volume 1, Number 3, July 2010. 9. Jyoti Singhai and Rakesh singhai,”MSVQ: A Data compression technique foe multimedia application”,Journal of Scientific & Industrial Research, Vol. 65,December 2006,pp. 982-985.
- 35. THANK YOU ALL…..