Abstract
This article presents a study on the impact of video frame losses on the quality perceived by users. Video compression standards, such as MPEG, use a sequence of frames called Group of Pictures (GOP), which is a video compression method which a frame is expressed in terms of one or more neighboring frames. This dependence between frames impacts directly in the quality because a loss of a reference frame prevents the decoding of other frames in GOP, thereby reducing the user-perceived quality. The assessment of quality in this article is estimated by Peak Signal Noise Ratio (PSNR), which compares the original and the received images. Computer simulations were used to show that the degradation on the quality may vary for different patterns of GOPs and type of lost frames.
Highlights
Multimedia transmission systems represent a significant portion of the use of current telecommunication systems
The results are showed in three different phases: first is presented a comparative of the number of frames generated for each test setup used; second, the frames size is verified; and is presented the resulting Peak Signal Noise Ratio (PSNR) for each test
It’s possible to notice that different Group of Pictures (GOP) configuration leads to different PSNR maximum values
Summary
Multimedia transmission systems represent a significant portion of the use of current telecommunication systems. The evolution in software and hardware technologies for data transmission allowed the improvement of new multimedia services like IPTV (Internet Protocol Television) and VOD (Video on Demand) [1] [2]. The advances and emerging technologies have been gaining a great deal of space in the development of services and applications. Among these services, it is possible to highlight the video streaming, which has a high bandwidth consumption [3] [4]. The user-perceived quality of video streaming applications is very sensitive to delay, loss, and throughput. The analysis of the image quality during data transmission can help to estimate user-perceived quality of video streaming. A better understanding of the effects of frame loss in the user-perceived quality can be used to improve the network configuration [10]
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