A Review on Various Image Compression Methods in Content Based Image Retrieval
Vandana Vinayak1, Sonika Jindal2
1Vandana Vinayak, Department of Computer Science & Engineering, Shaheed Bhagat Singh State Technical Campus, Ferozepur (Punjab), India.
2Prof. Sonika Jindal, Department of Computer Science & Engineering, Shaheed Bhagat Singh State Technical Campus, Ferozepur (Punjab), India.
Manuscript received on 10 December 2016 | Revised Manuscript received on 18 December 2016 | Manuscript Published on 30 December 2016 | PP: 58-62 | Volume-6 Issue-2, December 2016 | Retrieval Number: B4789126216/16©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper provides an overview about the various compression techniques available in the research area of Image retrieval, especially Content-Based Image Retrieval (CBIR), an evocative and authentic research area for the last decades. CBIR is used for the retrieval of the images based on the content of the images generally known as features. These features may be low level features i.e. color, shape, texture and spatial relationship or the high level features that use the concept of human brain. Now a days, the development and demand of multimedia product grows increasingly fast, contributing to insufficient storage of memory device. Therefore, the theory of data compression becomes more and more significant for reducing the data redundancy to save more hardware space. Compression is the process of reducing the amount of data required to represent the quality of information. Compression is also useful as it helps to reduce the consumption of expensive resources such as hard disk space.
Keywords: Especially Content-Based Image Retrieval (CBIR), Therefore, increasingly fast, provides.
Scope of the Article: Image Processing