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The main objective of this thesis is to provide a smooth video playout on the mobile device over wireless networks. The parameters that specify the wireless channel include: bandwidth variation, frame losses, and outage time. These parameters may affect the quality of the video negatively, and the mobile users may notice sudden stops during the playout video, i.e., the picture is momentarily frozen, followed by a jump from one scene to a different one. This thesis focuses on eliminating frozen pictures and reducing the amount of video data that need to be transmitted. In order to eliminate frozen scenes on the mobile screen, we propose three different techniques. In the first technique, the video frames are split into sub-frames; these sub-frames are streamed over different channels. In the second technique the sub-frames will be “crossed” and sent together with other sub-frames that are from different positions in the streaming video sequence. If some sub-frames are lost during the transmission a reconstruction mechanism will be applied on the mobile device to recreate the missing sub-frames. In the third technique, we propose a Time Interleaving Robust Streaming (TIRS) technique to stream the video frames in different order. The benefit of that is to avoid losing a sequence of neighbouring frames. A missing frame from the streaming video will be reconstructed based on the surrounding frames on the mobile device. In order to reduce the amount of video data that are streamed over limited bandwidth channels, we propose two different techniques. These two techniques are based on identifying and extracting a high motion region of the video frames. We call this the Region Of Interest (ROI); the other parts of the video frames are called the non-Region Of Interest (non-ROI). The ROI is transmitted with high quality, whereas the non-ROI is interpolated from a number of references frames. In the first technique the ROI is a fixed size region; we considered four different types of ROI and three different scenarios. The scenarios are based on the position of the reference frames in the streaming frame sequence. In the second technique the ROI is identified based on the motion in the video frames, therefore the size, position, and shape of the ROI will be different from one video to another according to the video characteristic. The videos are coded using ffmpeg to study the effect of the proposed techniques on the encoding size. Subjective and objective metrics are used to measure the quality level of the reconstructed videos that are obtained from the proposed techniques. Mean Opinion Score (MOS) measurements are used as a subjective metric based on human opinions, while for objective metric the Structural Similarity (SSIM) index is used to compare the similarity between the original frames and the reconstructed frames.

This study was carried out in collaboration with Tracab, a brand leader in real-time digital sports data. As a result, the application field is centred on sports analytics. The technology, for instance, consists of multiple cameras that capture a football pitch in a panoramic setup. The alignment of two or more cameras in both a spatial and temporal manner is referred to as sub-frame synchronisation. Because the cameras are already in the same geometric coordinates, only temporal synchronisation will be addressed in this project. The main method for retrieving the desynchronisation information that affects the cameras is based on optical flow. The off-sync cameras' spacial information is then synthesised to the time required by the synchronisation constraint using motion interpolation. In addition, the created system is compared to a real-time intermediate flow interpolation approach. The latter method relies on machine learning techniques, whereas this study focuses on more traditional methods. The metrics Peak Signal-to-Noise Ratio and Structural Similarity Index Measure are used to address the quality criteria required by this subject of study. Furthermore, visually perceived quality is examined to identify differences between measured and perceived quality. The results reveal that in every realistic situation investigated, temporal synchronisation can be addressed by an error measure of less than 1ms. The frame synthesis stage, on the other hand, fails to accurately estimate complicated scenarios, while the machine learning approach stands out. The implemented approach, on the other hand, addresses fast-moving objects with greater precision. Furthermore, the machine learning approach is unable to interpolate intermediate frames in arbitrary time steps, which is critical for the project's application. Finally, considering the lack of real-time computational speed and the quality achieved by machine learning approaches, more research is required in these directions. / Denna studie genomfördes i samarbete med Tracab, en marknadsledare inom digital sportdata levererad i realtid. Studiens applikationsområde kommer där av centreras kring sportdata där två eller flera kameror filmar en fotbollsplan i ett videopanorama. Kamerasynkroniseringen måste ske både spatialt och temporalt. Eftersom kamerorna har samma position kommer endast den temporala synkronisering tas upp i detta projekt. Den övergripande metoden för att göra detta är baserat på optiskt flöde. Data från en ej synkroniserad kamera syntetiseras via en synkroniseringkonstant mha. rörelseinterpolering. Detta jämförs även mot ett tillvägagångssätt som bygger på maskininlärning medan man i denna studie fokuserar på en mer traditionell lösningsmetod. Mätvärdena Peak Signal-to-Noise Ratio och Structural Similarity Index Measure används som kvalitetskriteria. Även visuellt upplevd kvalitet undersöks för att identifiera skillnaden mellan mätt och upplevd kvalitet. Resultatet visar att vid realistiska situationer kan den temporala synkroniseringen beräknas till under 1ms. Den syntetiserade datan lyckas dock inte estimera komplicerade situationer, medan maskininlärningsmetoden presterar bra. Dock så klarar studiens lösningsmetod att bättre generera objekt i snabb rörelse. Vidare så kan inte maskininlärningsmetoden generera video med en godtycklig tidförskjutning, något som är avgörande för projektets tillämpningsområde. Slutligen, med tanke på svårigheter i realtidsberäkning kontra kvaliteten hos maskin- inlärningsmetoder krävs därför mer forskning inom området.