True Color Image Compression and Decompression Using Fusion of Three-Level Discrete Wavelet Transform—Discrete Cosine Transforms and Arithmetic Coding Technique
16 May 2018-pp 469-481
TL;DR: The implemented and analysis of true color image compression and decompression technique has use two level of quantization this show significance improve in performance of compression algorithm.
Abstract: In this research paper, we have done the implementation and analysis of true color image compression and decompression technique. The implemented paper divides the color image into RGB component then after applying three-level Discrete Wavelet Transform, RGB components are split into nine higher frequency sub-bands and one lower order sub-band. The lower frequency sub-band is compressed into T-Matrix using One Dimension Discrete Cosine Transform. At the same time, higher frequency sub-bands are compressed using scalar quantize and eliminate zero and store data algorithm are applied to remove zeros in sub-band matrixes. Last, the encoded mode adopted arithmetic encoding. This algorithm has use two level of quantization this show significance improve in performance of compression algorithm. The decompression process is reverse process of encoder. The decompression algorithm decoded high-frequency subbands using return zero matrix algorithm and recover low-frequency sub-bands and other sub-bands using applying inverse process.
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01 Jan 1991TL;DR: The author explains the design and implementation of the Levinson-Durbin Algorithm, which automates the very labor-intensive and therefore time-heavy and expensive process of designing and implementing a Quantizer.
Abstract: 1 Introduction- 11 Signals, Coding, and Compression- 12 Optimality- 13 How to Use this Book- 14 Related Reading- I Basic Tools- 2 Random Processes and Linear Systems- 21 Introduction- 22 Probability- 23 Random Variables and Vectors- 24 Random Processes- 25 Expectation- 26 Linear Systems- 27 Stationary and Ergodic Properties- 28 Useful Processes- 29 Problems- 3 Sampling- 31 Introduction- 32 Periodic Sampling- 33 Noise in Sampling- 34 Practical Sampling Schemes- 35 Sampling Jitter- 36 Multidimensional Sampling- 37 Problems- 4 Linear Prediction- 41 Introduction- 42 Elementary Estimation Theory- 43 Finite-Memory Linear Prediction- 44 Forward and Backward Prediction- 45 The Levinson-Durbin Algorithm- 46 Linear Predictor Design from Empirical Data- 47 Minimum Delay Property- 48 Predictability and Determinism- 49 Infinite Memory Linear Prediction- 410 Simulation of Random Processes- 411 Problems- II Scalar Coding- 5 Scalar Quantization I- 51 Introduction- 52 Structure of a Quantizer- 53 Measuring Quantizer Performance- 54 The Uniform Quantizer- 55 Nonuniform Quantization and Companding- 56 High Resolution: General Case- 57 Problems- 6 Scalar Quantization II- 61 Introduction- 62 Conditions for Optimality- 63 High Resolution Optimal Companding- 64 Quantizer Design Algorithms- 65 Implementation- 66 Problems- 7 Predictive Quantization- 71 Introduction- 72 Difference Quantization- 73 Closed-Loop Predictive Quantization- 74 Delta Modulation- 75 Problems- 8 Bit Allocation and Transform Coding- 81 Introduction- 82 The Problem of Bit Allocation- 83 Optimal Bit Allocation Results- 84 Integer Constrained Allocation Techniques- 85 Transform Coding- 86 Karhunen-Loeve Transform- 87 Performance Gain of Transform Coding- 88 Other Transforms- 89 Sub-band Coding- 810 Problems- 9 Entropy Coding- 91 Introduction- 92 Variable-Length Scalar Noiseless Coding- 93 Prefix Codes- 94 Huffman Coding- 95 Vector Entropy Coding- 96 Arithmetic Coding- 97 Universal and Adaptive Entropy Coding- 98 Ziv-Lempel Coding- 99 Quantization and Entropy Coding- 910 Problems- III Vector Coding- 10 Vector Quantization I- 101 Introduction- 102 Structural Properties and Characterization- 103 Measuring Vector Quantizer Performance- 104 Nearest Neighbor Quantizers- 105 Lattice Vector Quantizers- 106 High Resolution Distortion Approximations- 107 Problems- 11 Vector Quantization II- 111 Introduction- 112 Optimality Conditions for VQ- 113 Vector Quantizer Design- 114 Design Examples- 115 Problems- 12 Constrained Vector Quantization- 121 Introduction- 122 Complexity and Storage Limitations- 123 Structurally Constrained VQ- 124 Tree-Structured VQ- 125 Classified VQ- 126 Transform VQ- 127 Product Code Techniques- 128 Partitioned VQ- 129 Mean-Removed VQ- 1210 Shape-Gain VQ- 1211 Multistage VQ- 1212 Constrained Storage VQ- 1213 Hierarchical and Multiresolution VQ- 1214 Nonlinear Interpolative VQ- 1215 Lattice Codebook VQ- 1216 Fast Nearest Neighbor Encoding- 1217 Problems- 13 Predictive Vector Quantization- 131 Introduction- 132 Predictive Vector Quantization- 133 Vector Linear Prediction- 134 Predictor Design from Empirical Data- 135 Nonlinear Vector Prediction- 136 Design Examples- 137 Problems- 14 Finite-State Vector Quantization- 141 Recursive Vector Quantizers- 142 Finite-State Vector Quantizers- 143 Labeled-States and Labeled-Transitions- 144 Encoder/Decoder Design- 145 Next-State Function Design- 146 Design Examples- 147 Problems- 15 Tree and Trellis Encoding- 151 Delayed Decision Encoder- 152 Tree and Trellis Coding- 153 Decoder Design- 154 Predictive Trellis Encoders- 155 Other Design Techniques- 156 Problems- 16 Adaptive Vector Quantization- 161 Introduction- 162 Mean Adaptation- 163 Gain-Adaptive Vector Quantization- 164 Switched Codebook Adaptation- 165 Adaptive Bit Allocation- 166 Address VQ- 167 Progressive Code Vector Updating- 168 Adaptive Codebook Generation- 169 Vector Excitation Coding- 1610 Problems- 17 Variable Rate Vector Quantization- 171 Variable Rate Coding- 172 Variable Dimension VQ- 173 Alternative Approaches to Variable Rate VQ- 174 Pruned Tree-Structured VQ- 175 The Generalized BFOS Algorithm- 176 Pruned Tree-Structured VQ- 177 Entropy Coded VQ- 178 Greedy Tree Growing- 179 Design Examples- 1710 Bit Allocation Revisited- 1711 Design Algorithms- 1712 Problems
7,015 citations
Book•
01 May 2000368 citations
01 Jan 1998
TL;DR: In this article, the present state and the future of colour image processing are discussed, with a focus on image segmentation and edge detection, as well as the application of colour in the textile industry.
Abstract: Preface. Acknowledgements. 1. The present state and the future of colour image processing. Part One: Colour. 2. Colour vision W. McIlhagga. 3. Colour science M. Ronnier Luo. 4. Colour spaces H. Palus. Part Two: Image Acquisition. 5. Colour video systems and signals R.E.N. Horne. 6. Image sources C. Connolly. 7. Practical system considerations C. Connolly, H. Palus. Part Three: Processing. 8. Noise removal and contrast enhancement J.M. Gauch. 9. Segmentation and edge detection J.M. Gauch. 10. Vector filtering K.N. Plataniotis, A.N. Venetsanopoulos. 11. Morphological operations M.L. Comer, E.J. Delp. 12. Frequency domain methods S.J. Sangwine, A.L. Thornton. 13. Compression M. Domanski, M. Bartkowiak. Part Four: Applications. 14. Colour management for the textile industry P.A. Rhodes. 15. Colour management for the graphic arts J. Morovic. 16. Medical imaging case study B.F. Jones, P. Plassmann. 17. Industrial colour inspection case studies C. Connolly. References. Index.
341 citations
12 Jul 2011
76 citations