scholarly journals Novel Progressive Region of Interest Image Coding Based on Matching Pursuits

2006 ◽  
Author(s):  
Abbas Ebrahimi-Moghadam ◽  
Shahram Shirani
Keyword(s):  
Image Coding ◽  
Region Of Interest ◽  
Matching Pursuits

A Region-of-interest Image Coding Algorithm Based on EBCOT

2010 ◽  
Vol 36 (5) ◽  
pp. 650-654 ◽  
Author(s):  
Chao SUN ◽  
Shou-Da JIANG ◽  
Jian-Feng WANG
Keyword(s):  
Image Coding ◽  
Region Of Interest

An Improved Method for CT Image Coding Using PSNR Prediction Model Based on ELM

2015 ◽  
Vol 738-739 ◽  
pp. 598-601
Author(s):  
Han Yang Zhu ◽  
Xin Yu Jin ◽  
Jian Feng Shen
Keyword(s):  
Computational Complexity ◽  
Prediction Model ◽  
Image Coding ◽  
Region Of Interest ◽  
Image Encoding ◽  
Large Size ◽  
Learning Machine ◽  
Intractable Problem ◽  
Encoding Efficiency ◽  
Better Than

In telemedicine, medical images are always considered very important telemedicine diagnostic evidences. High transmission delay in a bandwidth limited network becomes an intractable problem because of its large size. It’s important to achieve a quality balance between Region of Interest (ROI) and Background Region (BR) when ROI-based image encoding is being used. In this paper, a research made on balancing method of LS-SVM based ROI/BR PSNR prediction model to optimize the ROI encoding shows it’s much better than conventional methods but with very high computational complexity. We propose a new method using extreme learning machine (ELM) with lower computational complexity to improve encoding efficiency compared to LS-SVM based model. Besides, it also achieves the same effect of balancing ROI and BR.

scholarly journals Autofocus on Depth of Interest for 3D Image Coding

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Khouloud Samrouth ◽  
Olivier Deforges ◽  
Yi Liu ◽  
Mohamad Khalil ◽  
Wassim EL Falou
Keyword(s):  
Image Coding ◽  
Region Of Interest ◽  
Depth Map ◽  
3D Image ◽  
Coding Scheme ◽  
Analysis Process ◽  
Adaptive Resolution ◽  
Local Quality ◽  
Extraction Scheme ◽  
3D Sequence

For some 3D applications, one may want to focus on a specific depth zone representing a region of interest in the scene. In this context, we introduce a new functionality called “autofocus” for 3D image coding, exploiting the depth map as an additional semantic information provided by the 3D sequence. The method is based on a joint “Depth of Interest” (DoI) extraction and coding scheme. First, the DoI extraction scheme consists of a precise extraction of objects located within a DoI zone, given by the viewer or deduced from an analysis process. Then, the DoI coding scheme provides a higher quality for the objects in the DoI at the expense of other depth zones. The local quality enhancement supports both higher SNR and finer resolution. The proposed scheme embeds the Locally Adaptive Resolution (LAR) codec, initially designed for 2D images. The proposed DoI scheme is developed without modifying the global coder framework, and the DoI mask is not transmitted, but it is deduced at the decoder. Results showed that our proposed joint DoI extraction and coding scheme provide a high correlation between texture objects and depth. This consistency avoids the distortion along objects contours in depth maps and those of texture images and synthesized views.

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