scholarly journals Costs and Advantages of Object-Based Image Coding with Shape-Adaptive Wavelet Transform

2007 ◽  
Vol 2007 ◽  
pp. 1-13 ◽  
Author(s):  
Marco Cagnazzo ◽  
Sara Parrilli ◽  
Giovanni Poggi ◽  
Luisa Verdoliva
Keyword(s):  
Wavelet Transform ◽  
Image Coding ◽  
Adaptive Wavelet ◽  
Object Based ◽  
Adaptive Wavelet Transform

scholarly journals Costs and Advantages of Object-Based Image Coding with Shape-Adaptive Wavelet Transform

2007 ◽  
Vol 2007 (1) ◽  
pp. 078323 ◽  
Author(s):  
Marco Cagnazzo ◽  
Sara Parrilli ◽  
Giovanni Poggi ◽  
Luisa Verdoliva
Keyword(s):  
Wavelet Transform ◽  
Image Coding ◽  
Adaptive Wavelet ◽  
Object Based ◽  
Adaptive Wavelet Transform

Object-based image coding

1997 ◽  
Vol 41 (3) ◽  
pp. 455-461
Author(s):  
Montse Pardàs
Keyword(s):  
Image Coding ◽  
Object Based

PROGRESS IN HIGH COMPRESSION IMAGE CODING

1988 ◽  
Vol 02 (03) ◽  
pp. 387-405 ◽  
Author(s):  
MURAT KUNT
Keyword(s):  
Image Coding ◽  
Recent Progress ◽  
Scene Analysis ◽  
Directional Sensitivity ◽  
New Class ◽  
Texture Modeling ◽  
Brain Mechanism ◽  
Object Based ◽  
Original Picture ◽  
The Brain

The digital representation of an image requires a very large number of bits. The goal of image coding is to reduce this number, as much as possible, and to reconstruct a faithful duplicate of the original picture. Early efforts in image coding, solely guided by information theory, led to a plethora of methods. The compression ratio reached a plateau around 10: 1 a couple of years ago. Recent progress in the study of the brain mechanism of vision and scene analysis has opened new vistas in picture coding. Directional sensitivity of the neurones in the visual pathway combined with the separate processing of contours and textures has led to a new class of coding methods capable of achieving compression ratios as high as 100: 1. This paper presents recent progress on some of the main avenues of object-based methods. These second generation techniques make use of contour-texture modeling, new results in neurophysiology and psychophisics and scene analysis.

Edge Detection Based on Fuzzy Logic and Hybrid Types of Shannon Entropy

2020 ◽  
Vol 29 (14) ◽  
pp. 2050227
Author(s):  
Mourad Moussa ◽  
Hazar El Ouni ◽  
Ali Douik
Keyword(s):  
Computer Vision ◽  
Fuzzy Logic ◽  
Edge Detection ◽  
Image Coding ◽  
Conditional Entropy ◽  
Contour Detection ◽  
Object Based ◽  
Computer Vision Applications ◽  
The Given ◽  
First Time

Edge is basically the symbol and reflection of partial image discreteness. It is one of the most commonly used operations in image processing and pattern recognition, it contains a wealth of internal information leading to strong interpretation of image. Resisting against noise, illumination and extracting appropriate features from an image is a great challenge in many computer vision applications. Indeed this topic participates to reduce the handled information and focuses on those related to existing objects. Efficient and accurate edge detection will lead to increase in the performance of many computer vision applications, including image segmentation, object-based image coding and image retrieval. Contour detection contributes to locate pixel sets which correspond to sudden intensities variation, these unstable properties of the given image commonly suggest to important events on going in the scene. In this paper, we present in the first time a novel and robust method for edge detection based on joint and conditional entropy when we highlight a Shannon theory, the second part of this paper is dedicated to decision making of edge pixels membership by intelligent method based on fuzzy logic tool.

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