Abstract
In this study, an adaptive video quality algorithm is developed for Ultra High Definition (UHD) video broadcasting through Digital Video Broadcasting by Satellite 2nd Generation (DVB-S2), where three conditions are responsible for enhancing or reducing the quality of a video signal received by the DVB-S2 Set-Top-Box (STB). The conditions are: Coverage area, Distance between transmitter and receiver and Separation distance. These conditions are responsible for the required Signal to Noise Ratio (SNR), resultant Bit Error Rate (BER) and the overall capacity of the system. Based on these conditions, received parameters of an HD or UHD video vary; and the quality viewed by the user changes. Therefore, in this study, we have proposed an algorithm based on the future broadcast scenario where the broadcasters will be dealing with simulcasting of multiple video standards of HD and UHD, varying in resolution, frame rate, codec and more. This algorithm is developed using the Principle of Inclusion.
Highlights
The future of video broadcasting not just lies in resolution, and in new technologies like High Frame Rate (HFR), Wide Colour Gamut (WCG), High Efficiency Video Coding (HEVC) and more
A statistical adaptive video quality algorithm is developed for Ultra High Definition (UHD) video broadcasting through Digital Video Broadcasting by Satellite 2nd Generation (DVB-S2), using the principle of inclusion
Using the generic DVB-S2 model, as explained in detail in (Pal and King, 2015b), information bits are extracted from a UHD video and transmitted through the MATLAB built DVB-S2 model, as given in Fig. 2 (DVB, 2005)
Summary
The future of video broadcasting not just lies in resolution, and in new technologies like High Frame Rate (HFR), Wide Colour Gamut (WCG), High Efficiency Video Coding (HEVC) and more. An adaptive video quality model using the principle of inclusion as given in this study, resolves this problem by efficiently allocating the available resources (channel capacity, coverage area, distance between transmitter and receiver and separation distance), by trading off the video quality. Using the BER vs SNR results, its correlation with the channel capacity, separation distance and coverage area is analysed.
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