H.264 video compression11/27/2023 In order to specify, evaluate and compare video communication systems it is necessary toĭetermine the quality of the video images displayed to the viewer. The Common Intermediate Format (CIF) is the basis for a popular set of formats: Format In practice, it is common to capture or convert to one of a set of ‘intermediate formats’ prior to compression and transmission. The video compression algorithms can compress a wide variety of video frame formats. ![]() Y resolution: 720x576 px, each pixel represented with 8 bits (0-255 decimal)Ĥ:4:4 Cr, Cb, Y total number of bits: 720x576x8x3 = 9953280 bitsĤ:2:0 Cr, Cb, Y total number of bits: 720x572x8 + 360x288x8x2 = 4976640 bitsĤ:2:0 sampling is sometimes described as 12 bits per pixel. Let's reason about the number of bits required to represent an image: Image resolution: 720x576 px 4:2:0 is widely used in video conferencing, digital television and DVD storage.įrom the example above we see thay 4:2:0 chroma subsampling requires half as many bits as 4:4:4 (non compressed) format. y is the number of changes of chroma samples between the first and seconds rows of a pixels.Īn exception to this exists with 4:1:0, which provides a single chroma sample within each 4 x 4 block of luma resolution.Īnother visual explanation of chroma subsampling:Ĥ:2:0 is widely used sampling format (YUV, YVI12) where Cr and Cb have each half the horizontal and vertical resolution of Y.x is the number of chroma samples in the first row of a pixels (horizontal resolution in relation to a).a is the horizontal sampling reference (usually 4).These schemas are known as subsampling systems and are expressed as a 3 part ratio - a:x:y which defines the chroma resolution in relation to a a x 2 block of luma pixels. In a 4:2:0 ratio, a quarter of the color data is present compared to a 4:4:4 ratio. In a 4:2:2 ratio, half of the colour data is present compared to a 4:4:4 ratio. A 4:4:4 ratio is actually uncompressed, as the amount of chroma data is equal to the amount of brightness data. YCrCb sampling formatsĬhroma subsampling compression levels are referred to as ratios, such as 4:4:4, 4:2:2 and 4:2:0. ![]() Representing chroma with a lower resolution than luma is simple but effective form of image compression. This reduces the amount of data required to represent the chrominance components without having a too obvious effect on visual quality. Y:Cr:Cb has an important advantage over RGB in that the Cr and Cb components may be represented with lower resolution than Y because the HVS is less sensitive to color than luminance. So only the luma (Y) and red and blue Cr,Cb are transmitted. However, Cr,Cg,Cb is a constant and so only two of the three chorminance components need to be stored or transmitted since the third component can always calculated from the other two. So far this representation has little obvious merit since we now have four components instead of three in RGB. The complete description of a color image is given by Y, the luminance component and three color differences, Cr, Cb and Cg that represent the difference between the color intensity and the mean luminance. We can use the coefficients from the standard BT.601 that was recommended by the group ITU-R *. Once we had the luma, we can split the colors (chroma blue and red): b = 0.564(B - Y)Īnd we can also convert it back and even get the green by using YCbCr. We can produce all the colors without using the green. The YCbCr can be derived from RGB and it also can be converted back to RGB. This color model uses Y to represent the brightness and two color channels Cb (chroma blue) and Cr (chroma red). It's a more efficient way of representing a frame for the human eye. ![]() Human Visual System (HVS) is less sensitive to color than luminance. Red, Green and Blue can create any color in varying proportions. Progressive and Interlaced samplingĪ video signal capture might be using progressive sampling (series of complete frames) or interlaced sampling (series of horizontal odd or even frames capturing half of the frame information). In simpler terms spatially means capturing frames in a 2-D square, temporally capturing 2-D squares in regular intervals in time. CaptureĪ natural visual scene is spatially and temporally continuous. It's an ongoing article and I add to it when I have the time. The goal is to implement as much as I know how to these concepts in Python. Richardson - H264 book and other online resources. This is me exploring the concepts of H264 video compression based on I an E. Video coding and decoding is a process of compressing and decompressing a digital video signal.
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