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5 points to consider for HD video encoding<\/p>\n
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Encode<\/p>\n
The image quality of Internet videos is almost proportional to the bit rate. However, if the bit rate increases unnecessarily, the file size will increase. “Keep bit rate low”, “Reduce file size” and “Reduce load time” are linked, and there is nothing to say if you can encode in high definition while keeping the bit rate low.
\nAlso, at the beginning, I wrote that “image quality is almost proportional to bit rate”, but I think some of you may have experienced that “I increased the bit rate and encoded, but I am not satisfied with the quality of the image. “So, this time, I will explain five points that are often used to do high definition video encoding.<\/p>\n
Encoding Mechanism
\nFirst, I will briefly explain the encoding mechanism.
\nAn image is a collection of continuous images (frames), and by changing this in a short time like a flip book, it appears that you are visually moving. Japanese television images are 29.97 frames per second (short for 29.97 fps \/ frame per second) and most movies and animations are standard 24 fps. Since a large number of frames are required for video, the amount of data is also huge. Therefore, data compression is indispensable for distributing videos on the Internet.<\/p>\n
When coding<\/p>\n
“Prediction in frame” that compresses data within a frame
\n“Prediction between frames” that compresses data into consecutive frames
\nThe information is reduced to the extent that it does not affect the visual sense and the data is compressed.<\/p>\n
\u25a0 What is in-frame prediction?
\nThere are various methodologies for data compression, so I will skip the details here, but the basic idea of \u200b\u200bwithin-frame prediction is to divide a frame into small blocks called cells and the colors adjacent to each other in the block. they are the same or similar, they are compressed together.
\nFor example, if there is information “blue blue blue blue blue blue blue blue blue blue red red yellow yellow yellow” information in the divided block, the amount of data can be reduced by combining this with “blue 11, red 2, yellow 3”. It’s an image.<\/p>\n
An example of data compression in in-frame prediction (image)<\/p>\n
\u25a0 What is cross-frame prediction?
\nHowever, in the case of video with a time axis, the number of frames is large, so there is a limit to the overall weight reduction based solely on the prediction within the frame. On the other hand, in the prediction between frames, based on the idea that “the contents are similar before and after the consecutive frames”, the cells that do not change from the previous frame reduce the amount of data by reusing information, and the cells that change is converted to data as difference information.<\/p>\n
Point 1: learn a suitable bit rate guideline for resolution<\/p>\n
The above three are excerpts of the same 640x360px image, but the file sizes are different at 1MB, 250KB, and 50KB. The image quality of 250 kB is lower than that of 1 MB and is much coarser at 50 kB. This is because the amount of data allocated per pixel is reduced, resulting in poor image quality.<\/p>\n
The same phenomenon can be said of the videos. Bit rate is the amount of data allocated per second and affects the image quality of the video. If you want to view a video on a large screen, such as viewing it full screen on a computer or TV monitor, you need a sufficient bit rate according to the resolution. On the other hand, when displayed at a small resolution, the roughness is not as noticeable even if the bit rate is reduced. On the contrary, even if you encode at a high bit rate, you will not notice the difference in image quality, and in many cases the file size will only increase.<\/p>\n","protected":false},"excerpt":{"rendered":"