Fractal Coding Based Video Compression Using Weighted Finite Automata

Fractal Coding Based Video Compression Using Weighted Finite Automata

Shailesh D. Kamble (Computer Science & Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India), Nileshsingh V. Thakur (Computer Science & Engineering, Prof Ram Meghe College of Engineering and Management, Amravati, India) and Preeti R. Bajaj (Electronics Engineering, G. H. Raisoni College of Engineering, Nagpur, India)
Copyright: © 2018 |Pages: 19
DOI: 10.4018/IJACI.2018010107
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Main objective of the proposed work is to develop an approach for video coding based on Fractal coding using the weighted finite automata (WFA). The proposed work only focuses on reducing the encoding time as this is the basic limitation why the Fractal coding not becomes the practical reality. WFA is used for the coding as it behaves like the Fractal Coding (FC). WFA represents an image based on the idea of fractal that the image has self-similarity in itself. The plane WFA (applied on every frame), and Plane FC (applied on every frame) coding approaches are compared with each other. The experimentations are carried out on the standard uncompressed video databases, namely, Traffic, Paris, Bus, Akiyo, Mobile, Suzie etc. and on the recorded video, namely, Geometry and Circle. Developed approaches are compared on the basis of performance evaluation parameters, namely, encoding time, decoding time, compression ratio, compression percentage, bits per pixel and Peak Signal to Noise Ratio (PSNR). Though the initial number of states is 256 for every frame of all the types of videos, but we got the different number of states for different frames and it is quite obvious due to minimality of constructed WFA for respective frame. Based on the obtained results, it is observed that the number of states is more in videos namely, Traffic, Bus, Paris, Mobile, and Akiyo, therefore the reconstructed video quality is good in comparison with other videos namely, Circle, Suzie, and Geometry.
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Video compression deals with the compression mechanism for the series of image sequences. In coding, the correlation between the adjacent image frames may get explored, as well as the relativity between them may also be used in the development of the compression mechanism. Generally, the adjacent image frame does not differ much. The probable difference lies in the displacement of the object in the given image frame with respect to the previous image frame. Grey and color videos (i.e. image sequences of grey or color image frame) are the customers for the video compression approach. Different color spaces can be used for the video images from processing point of view (Gonzalez et al., 2005; Moltredo et al., 1997; Zhang et al., 1995; Danciu et al., 1998; Hurtgen et al., 1994).

In the image sequences, there are two types of redundancies: Temporal redundancy and spatial redundancy. Spatial redundancy means redundancy among neighboring pixel in a frame. The coding technique which reduces the spatial redundancy is called as intra-frame coding. The intra-frame coding is similar to the individual image coding i.e. coding within the frame for finding out the redundancy in an image itself. Temporal redundancy means redundancy between two consecutive frames in a sequence of frames. The coding technique which reduces the temporal redundancy is called as inter-frame coding. Video compression deals with the compression mechanism for the series of image sequences. Block matching motion estimation (Zhu et al., 2009, 2009a; Zhu et al., 2010; Zhu et al., 2000; Cheung et al., 2000, 2002; Po et al., 1996; Liu et al., 1996; Ce Zhu et al., 2002; Li Hong-ye et al., 2009; Belloulata et al., 2011; Acharjee et al., 2012, 2013, 2014; Kamble et al., 2016, 2017) plays an important role in inter-frame coding technique to reduce temporal redundancy present in the series of image sequences.

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