Amirjanov et al., 2019 - Google Patents
Image compression system with an optimisation of compression ratioAmirjanov et al., 2019
View PDF- Document ID
- 15752504430953029531
- Author
- Amirjanov A
- Dimililer K
- Publication year
- Publication venue
- IET Image Processing
External Links
Snippet
The fundamental goal of image data compression is to set an optimal compression ratio while maintaining an acceptable reproduction quality. This study describes the principles of design of image compression system that automatically sets an optimal compression ratio …
- 238000007906 compression 0 title abstract description 186
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Nair et al. | Multi‐sensor medical image fusion using pyramid‐based DWT: a multi‐resolution approach | |
Amirjanov et al. | Image compression system with an optimisation of compression ratio | |
Chouhan et al. | Enhancement of dark and low‐contrast images using dynamic stochastic resonance | |
Diwakar et al. | CT image denoising using NLM and correlation‐based wavelet packet thresholding | |
Dimililer | Backpropagation neural network implementation for medical image compression | |
Wen et al. | Image recovery via transform learning and low-rank modeling: The power of complementary regularizers | |
Singh et al. | CT and MR image information fusion scheme using a cascaded framework in ripplet and NSST domain | |
Miosso et al. | Compressive Sensing With Prior Information: Requirements and Probabilities of Reconstruction in 𝓁 1-Minimization | |
Ibaida et al. | Cloud enabled fractal based ECG compression in wireless body sensor networks | |
Zhang et al. | Image denoising based on sparse representation and gradient histogram | |
Vaiter et al. | Low complexity regularization of linear inverse problems | |
Li et al. | A novel medical image denoising method based on conditional generative adversarial network | |
Deeba et al. | Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm | |
Li et al. | Blind image quality assessment based on joint log-contrast statistics | |
Cao et al. | Acceleration of histogram‐based contrast enhancement via selective downsampling | |
Wan et al. | Feature consistency training with JPEG compressed images | |
Wang et al. | Image fusion via feature residual and statistical matching | |
Kang et al. | Fusion framework for multi‐focus images based on compressed sensing | |
Ding et al. | Full‐reference image quality assessment using statistical local correlation | |
Zhao et al. | Image quality assessment based on complementary local feature extraction and quantification | |
Tirumani et al. | Image resolution and contrast enhancement with optimal brightness compensation using wavelet transforms and particle swarm optimization | |
Qian et al. | Towards efficient blind quality evaluation of screen content images based on edge‐preserving filter | |
da Silva et al. | No‐reference video quality assessment method based on spatio‐temporal features using the ELM algorithm | |
Papakostas et al. | Computation strategies of orthogonal image moments: A comparative study | |
Shi et al. | Compressed sensing magnetic resonance imaging based on dictionary updating and block‐matching and three‐dimensional filtering regularisation |