# Transitioning Broadcast to Cloud
## 3 Technologies Needed to Support Convergence
### Signal Processing  
As also mentioned earlier, broadcast workflows may carry videos in progressive, interlaced, or telecine formats. Field orders and pulldown patterns may also differ across different sources. When such signals are then edited or combined together, this produces output with changing temporal sampling type. If such videos are then encoded and delivered as interlaced--they may still look ok on traditional TV sets. However, if one receives such interlace-encoded signals in and then \"naively\" tries to convert them to progressive, the results can be disastrous, e.g., a wrong assumption about field order can make videos look jerky, lack of detection of 3:2 pulldowns can produce periodic garbled frames, etc.  
What also makes broadcast signals difficult to work with are accumulations of compression and conversion artifacts. The further down the chain the signal is obtained, the mode \"noisier\" it becomes.  
To work with such complex signals, a proper processing stack is needed. One possible architecture is illustrated in Figure 4. It includes a content analysis module, which performs  
Figure 3: An example of cloud-based redundant live streaming workflow.  
detection of segment cuts and identifies types of temporal sampling patterns and artifacts in each segment. Such information, along with bitstream metadata is then passed to a chain of filters, including artifact removal, temporal sampling conversion, color space conversion, and scaling filters.  
The artifact removal filters, such as deblocking and denoising operations are among most basic techniques needed to work with broadcast signals. Deblocking filters are needed, e.g., in working with MPEG-2 encoded content, as MPEG-2 codec [54] does not have in-loop filters, and passes all such artifacts to the output. In Figure 5, we show how such artifacts look, along with cleaned output produced by our deblocking filer. Denoising is also needed, especially when working with older (analog-converted) SD signals. Removal of low-magnitude noise not only makes signal cleaner, but also makes the job of the subsequent encoder easier, enabling it to achieve better quality or lower rate. We illustrate this effect in Figure 6.  
Temporal sampling conversion filter in Figure 4 performs conversions between progressive, telecine, and interlace formats, as well as temporal interpolation and resampling operations. As discussed earlier, this filter is driven by information from the content analysis module. This way, e.g., telecine segment can be properly converted back to progressive, interlaced, properly de  
Figure 4: Decoding and format conversion chain needed to operate with cable/broadcast content.  
Figure 5: Example of MPEG2 blocking artifacts **(a)** and their removal by deblocking filter **(b)**.  
The quality of temporal sampling conversion operations is very critical. For example, in Figure 7, we show the outputs of a basic deinterlacing filter (FFMPEG \"yadif\" filter [83]) and more advanced optical-flow-based algorithm [84]. It can be seen that a basic deinterlacer cannot maintain continuity of field lines under high motion. The effects of such nature can be very prominent in sports broadcast content.  
Figure 6: Example of using denoising filter for improving quality of final transcoded signal.  
Figure 7: Comparison of outputs of basic (**a**) and advanced (**b**) deinterlacing filters.  
The use of subsequent filters in Figure 5, such as color space conversion and scaling filters, is driven by possible differences in color spaces, SARs, and resolutions in input and output formats.  
All such conversion operations need to be state of the art. Or at least they must be comparable in quality with Teranex (1998), Snell-Willcox/Grass Valley KudosPro (1999), and other standards converter boxes commonly used in post-production and broadcast.