Near Lossless Compression of Video Frames Using Soft Computing Technologies in Immersive Multimedia
Prakash Jadhav1, G. K. Siddesh2
1Prof. Prakash Jadhav, Electronics and communication Department, Visveswaraya Technological University, Belgaum, K. S. School of Engineering and Management, Bangalore, India.
2Dr. G. K. Siddesh, Electronics and communication Department, Bangalore University, J.S.S. Academy of Technical Education , Bangalore, India

Manuscript received on January 02, 2014. | Revised Manuscript received on January 04, 2014. | Manuscript published on January  05, 2014. | PP: 16-22 | Volume-4 Issue-6, January 2014. | Retrieval Number: F2455014615/2015©BEIESP
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Abstract: Compression of Video Frames is a heavily researched domain in Digital TV Technology and Multimedia applications and hundreds of researchers have experimented with various techniques of transform coding and quantization mechanisms with varied results. Despite this fact, there is still a tremendous amount of scope for better compression schemes that seek to reduce the utilized band of the spectrum. In real time applications such as Digital TV and streaming Multimedia applications, time plays a crucial role and a successful implementation usually makes a compromise between factors such as (1) quality of reconstructed frames, (2) amount of utilized band owing to compression and (3) ability of the system to cope with increasing frame rates and advanced video profiles that are creeping into Digital TV Standards. It is important to observe that methodologies employed in achieving successful compression based on both inter and intra-frame redundancies rely very heavily on the frequency-domain redundancies generated by transform coding techniques and it is imperative that compression techniques place a great amount of emphasis on the correlation that can generated on a sub-image basis. Noise in images tends to decrease the correlation and noisy frames tend to compress less. Our objective in this research program is to focus on ways and means of increasing the redundancies by employing Linear Neural Network techniques with feed-forward mechanisms. Since quantization plays a major role in the amount of compression generated and the quality of the reconstructed frames, adaptive quantization based on a PSNR threshold value is employed so that best compression with a guaranteed reconstructed quality is obtained. Our research program targets to achieve the objectives: (1) better compression ratios with even fairly noisy frames (2) better quality of reconstructed frames in terms of very large PSNR and RMS Error tending towards values extremely small and nearer to 0 and (3) honoring the time constraints imposed by frame rates.
Keywords: Neural Networks, Peak Signal to Noise Ratio (PSNR), Root Mean Square Error (RMS), Quantization, Discrete Cosine Transform (DCT), Burrows Wheeler Transform (BWT), Rosseta Vector, bandwidth.