Question

互联网上有很多关于YUV4：4：4到YUV4：2：2格式之间差异的信息，但是，我找不到任何有关如何将YUV4：4：4转换为YUV4的信息： 2：2。由于这种转换是使用软件执行的，我希望应该有一些开发人员已经完成它并且可以引导我到描述转换算法的源。当然，软件代码会很好，但是访问理论就足以编写我自己的软件了。具体来说，我想知道像素结构以及转换过程中字节的管理方式。

我发现了几个类似的问题，例如this和this，但无法回答我的问题。另外，我在Photography forum上发布了这个问题，他们认为这是一个软件问题。

Answer 1

您无法找到具体描述的原因是，有很多方法可以做到这一点让我们从维基百科开始：https://en.wikipedia.org/wiki/Chroma_subsampling#4:2:2

4：4：4：
三个Y＆lt; CbCr组件中的每一个具有相同的采样率，因此没有色度子采样。这种方案有时用于高端胶片扫描仪和电影后期制作。

和

4：2：2：
以亮度采样率的一半对两个色度分量进行采样：水平色度分辨率减半。这将未压缩视频信号的带宽减少了三分之一，几乎没有视觉差异。

注：术语YCbCr和YUV可互换使用 https://en.wikipedia.org/wiki/YCbCr

Y'CbCr经常与YUV颜色空间混淆，通常术语YCbCr和YUV可互换使用，导致一些混淆;当提到视频或数字形式的信号时，术语＆＃34; YUV＆＃34;主要是指＆＃34; Y'CbCr＆＃34;。

数据存储器排序：
同样有多种格式英特尔IPP文档定义了两个主要类别：＆＃34; Pixel-Order Image Formats＆＃34;和＆＃34;平面图像格式＆＃34;。
这里有一个很好的文档：https://software.intel.com/en-us/node/503876
有关YUV像素排列格式，请参阅此处：http://www.fourcc.org/yuv.php#NV12 请参阅此处：http://scc.ustc.edu.cn/zlsc/sugon/intel/ipp/ipp_manual/IPPI/ippi_ch6/ch6_image_downsampling.htm#ch6_image_downsampling以获得下采样说明。

让我们假设＆＃34; Pixel-Order＆＃34;格式：

YUV 4:4:4 data order: Y0 U0 V0  Y1 U1 V1  Y2 U2 V2  Y3 U3 V3  
YUV 4:2:2 data order: Y0  U0    Y1  V0    Y2  U1    Y3  V1

每个元素都是一个字节，Y0是内存中的低位字节上述4：2：2数据顺序被命名为UYVY或YUY2像素格式。

转换算法：

＆＃34; Naive sub-sampling＆＃34;：
＆＃34;投掷＆＃34;每秒U / V分量：
取U0，然后抛出U1，抓住V0并抛出V1 ...... 来源：Y0 U0 V0 Y1 U1 V1 Y2 U2 V2 目的地：Y0 U0 Y1 V0 Y2 U2 Y3 V2
我无法推荐它，因为它会导致aliasing瑕疵。
每个U / V对的平均值：
取目的地U0等于来源(U0+U1)/2，V0相同... ... 来源：Y0 U0 V0 Y1 U1 V1 Y2 U2 V2 目的地：Y0 (U0+U1)/2 Y1 (V0+V1)/2 Y2 (U2+U3)/2 Y3 (V2+V3)/2
使用其他插值方法对U和V进行下采样（例如，三次插值）通常，与简单平均值相比，您将无法看到任何差异。

C实施：

问题没有标记为C，但我认为以下C实现可能会有所帮助以下代码通过对每个U / V对进行平均，将像素排序的YUV 4：4：4转换为像素排序的YUV 4：2：2：

//Convert single row I0 from pixel-ordered YUV 4:4:4 to pixel-ordered YUV 4:2:2.
//Save the result in J0.
//I0 size in bytes is image_width*3
//J0 size in bytes is image_width*2
static void ConvertRowYUV444ToYUV422(const unsigned char I0[],
                                     const int image_width,
                                     unsigned char J0[])
{
    int x;

    //Process two Y,U,V triples per iteration:
    for (x = 0; x < image_width; x += 2)
    {
        //Load source elements
        unsigned char y0    = I0[x*3];                  //Load source Y element
        unsigned int u0     = (unsigned int)I0[x*3+1];  //Load source U element (and convert from uint8 to uint32).
        unsigned int v0     = (unsigned int)I0[x*3+2];  //Load source V element (and convert from uint8 to uint32).

        //Load next source elements
        unsigned char y1    = I0[x*3+3];                //Load source Y element
        unsigned int u1     = (unsigned int)I0[x*3+4];  //Load source U element (and convert from uint8 to uint32).
        unsigned int v1     = (unsigned int)I0[x*3+5];  //Load source V element (and convert from uint8 to uint32).

        //Calculate destination U, and V elements.
        //Use shift right by 1 for dividing by 2.
        //Use plus 1 before shifting - round operation instead of floor operation.
        unsigned int u01    = (u0 + u1 + 1) >> 1;       //Destination U element equals average of two source U elements.
        unsigned int v01    = (v0 + v1 + 1) >> 1;       //Destination U element equals average of two source U elements.

        J0[x*2]     = y0;   //Store Y element (unmodified).
        J0[x*2+1]   = (unsigned char)u01;   //Store destination U element (and cast uint32 to uint8).
        J0[x*2+2]   = y1;   //Store Y element (unmodified).
        J0[x*2+3]   = (unsigned char)v01;   //Store destination V element (and cast uint32 to uint8).
    }
}


//Convert image I from pixel-ordered YUV 4:4:4 to pixel-ordered YUV 4:2:2.
//I - Input image in pixel-order data YUV 4:4:4 format.
//image_width - Number of columns of image I.
//image_height - Number of rows of image I.
//J - Destination "image" in pixel-order data YUV 4:2:2 format.
//Note: The term "YUV" referees to "Y'CbCr".

//I is pixel ordered YUV 4:4:4 format (size in bytes is image_width*image_height*3):
//YUVYUVYUVYUV
//YUVYUVYUVYUV
//YUVYUVYUVYUV
//YUVYUVYUVYUV
//
//J is pixel ordered YUV 4:2:2 format (size in bytes is image_width*image_height*2):
//YUYVYUYV
//YUYVYUYV
//YUYVYUYV
//YUYVYUYV
//
//Conversion algorithm:
//Each element of destination U is average of 2 original U horizontal elements
//Each element of destination V is average of 2 original V horizontal elements
//
//Limitations:
//1. image_width must be a multiple of 2.
//2. I and J must be two separate arrays (in place computation is not supported). 
static void ConvertYUV444ToYUV422(const unsigned char I[],
                                  const int image_width,
                                  const int image_height,
                                  unsigned char J[])
{
    //I0 points source row.
    const unsigned char *I0;    //I0 -> YUYVYUYV...

    //J0 and points destination row.
    unsigned char *J0;          //J0 -> YUYVYUYV

    int y;  //Row index

    //In each iteration process single row.
    for (y = 0; y < image_height; y++)
    {
        I0 = &I[y*image_width*3];   //Input row width is image_width*3 bytes (each pixel is Y,U,V).

        J0 = &J[y*image_width*2];   //Output row width is image_width*2 bytes (each two pixels are Y,U,Y,V).

        //Process single source row into single destination row
        ConvertRowYUV444ToYUV422(I0, image_width, J0);
    }
}

YUV 4：2：2的平面表示

平面表示可能比＆＃34; Pixel-Order＆＃34;更直观。格式。
在平面表示中，每个颜色通道表示为单独的矩阵，其可以显示为图像。

示例：

RGB格式的原始图像（转换为YUV之前）：
YUV 4：4：4格式的图像频道：

（左YUV三重以灰度表示，右YUV三重以虚假颜色表示）。
YUV 4：2：2格式的图像频道（水平Chroma subsampling之后）：

（左YUV三重表示为灰度级，右YUV三重表示使用＆＃34;假颜色＆＃34;）。

如您所见，在4：2：2格式中，U和V通道在水平轴上进行下采样（收缩）。

注：
＆＃34;假色＆＃34; U和V信道的表示用于强调Y是Luma信道，U和V是Chrominance信道。

高阶插值和抗锯齿滤波器：
以下MATLAB代码示例演示了如何使用高阶插值和抗混叠滤波器执行下采样该示例还显示了FFMPEG使用的下采样方法注意：您不需要了解MATLAB编程以了解样本您需要通过Kernel和图像之间的卷积来了解图像过滤的一些知识。

%Prepare the input:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
load('mandrill.mat', 'X', 'map'); %Load input image
RGB = im2uint8(ind2rgb(X, map));  %Convert to RGB (the mandrill sample image is an indexed image)
YUV = rgb2ycbcr(RGB);             %Convert from RGB to YUV (MATLAB function rgb2ycbcr uses BT.601 conversion formula)

%Separate YUV to 3 planes (Y plane, U plane and V plane)
Y = YUV(:, :, 1);
U = YUV(:, :, 2);
V = YUV(:, :, 3);

U = double(U); %Work in double precision instead of uint8.

[M, N] = size(Y); %Image size is N columns by M rows.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%Linear interpolation without Anti-Aliasing filter:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Horizontal down-sampling U plane using Linear interpolation (without Anti-Aliasing filter).
%Simple averaging is equivalent to linear interpolation.
U2 = (U(:, 1:2:end) + U(:, 2:2:end))/2;
refU2 = imresize(U, [M, N/2], 'bilinear', 'Antialiasing', false); %Use MATLAB imresize function as reference
disp(['Linear interpolation max diff = ' num2str(max(abs(double(U2(:)) - double(refU2(:)))))]); %Print maximum difference.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%Cubic interpolation without Anti-Aliasing filter:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Horizontal down-sampling U plane using Cubic interpolation (without Anti-Aliasing filter).
%Following operations are equivalent to cubic interpolation:
%1. Convolution with filter kernel [-0.125, 1.25, -0.125]
%2. Averaging pair elements
fU = imfilter(U, [-0.125, 1.25, -0.125], 'symmetric');
U2 = (fU(:, 1:2:end) + fU(:, 2:2:end))/2;
U2 = max(min(U2, 240), 16); %Limit to valid range of U elements (valid range of U elements in uint8 format is [16, 240])
refU2 = imresize(U, [M, N/2], 'cubic', 'Antialiasing', false); %Use MATLAB imresize function as reference
refU2 = max(min(refU2, 240), 16); %Limit to valid range of U elements
disp(['Cubic interpolation max diff = ' num2str(max(abs(double(U2(:)) - double(refU2(:)))))]); %Print maximum difference.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%Linear interpolation with Anti-Aliasing filter:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Horizontal down-sampling U plane using Linear interpolation with Anti-Aliasing filter.
%Remark: The Anti-Aliasing filter is the filter used by MATLAB specific implementation of 'bilinear' imresize.
%Following operations are equivalent to Linear interpolation with Anti-Aliasing filter:
%1. Convolution with filter kernel [0.25, 0.5, 0.25]
%2. Averaging pair elements
fU = imfilter(U, [0.25, 0.5, 0.25], 'symmetric');
U2 = (fU(:, 1:2:end) + fU(:, 2:2:end))/2;
refU2 = imresize(U, [M, N/2], 'bilinear', 'Antialiasing', true); %Use MATLAB imresize function as reference
disp(['Linear interpolation with Anti-Aliasing max diff = ' num2str(max(abs(double(U2(:)) - double(refU2(:)))))]); %Print maximum difference.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%Cubic interpolation with Anti-Aliasing filter:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Horizontal down-sampling U plane using Cubic interpolation with Anti-Aliasing filter.
%Remark: The Anti-Aliasing filter is the filter used by MATLAB specific implementation of 'cubic' imresize.
%Following operations are equivalent to Linear interpolation with Anti-Aliasing filter:
%1. Convolution with filter kernel [-0.0234375, -0.046875, 0.2734375, 0.59375, 0.2734375, -0.046875, -0.0234375]
%2. Averaging pair elements
h = [-0.0234375, -0.046875, 0.2734375, 0.59375, 0.2734375, -0.046875, -0.0234375];
fU = imfilter(U, h, 'symmetric');
U2 = (fU(:, 1:2:end) + fU(:, 2:2:end))/2;
U2 = max(min(U2, 240), 16); %Limit to valid range of U elements
refU2 = imresize(U, [M, N/2], 'cubic', 'Antialiasing', true); %Use MATLAB imresize function as reference
refU2 = max(min(refU2, 240), 16); %Limit to valid range of U elements
disp(['Cubic interpolation with Anti-Aliasing max diff = ' num2str(max(abs(double(U2(:)) - double(refU2(:)))))]); %Print maximum difference.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


%FFMPEG implementation of horizontal down-sampling U plane.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%FFMPEG uses cubic interpolation with Anti-Aliasing filter (different filter kernel):
%Remark: I didn't check the source code of FFMPEG to verify the values of the filter kernel.
%I can't tell how FFMPEG actually implements the conversion.
%Following operations are equivalent to FFMPEG implementation (with minor differences):
%1. Convolution with filter kernel [-115, -231, 1217, 2354, 1217, -231, -115]/4096
%2. Averaging pair elements
h = [-115, -231, 1217, 2354, 1217, -231, -115]/4096;
U2 = (fU(:, 1:2:end) + fU(:, 2:2:end))/2;
U2 = max(min(U2, 240), 16); %Limit to valid range of U elements (FFMPEG actually doesn't limit the result)

%Save Y,U,V planes to file in format supported by FFMPEG
f = fopen('yuv444.yuv', 'w');
fwrite(f, Y', 'uint8');
fwrite(f, U', 'uint8');
fwrite(f, V', 'uint8');
fclose(f);

%For executing FFMPEG within MATLAB, download FFMPEG and place the executable in working directory (ffmpeg.exe for Windows)
%FFMPEG converts source file in YUV444 format to destination file in YUV422 format.
[status, cmdout] = system(['./ffmpeg -y -s ', num2str(N), 'x', num2str(M), ' -pix_fmt yuv444p -i yuv444.yuv -pix_fmt yuv422p yuv422.yuv']);
f = fopen('yuv422.yuv', 'r');
refY = (fread(f, [N, M], '*uint8'))';
refU2 = (fread(f, [N/2, M], '*uint8'))'; %Read down-sampled U plane (FFMPEG result from file).
refV2 = (fread(f, [N/2, M], '*uint8'))';
fclose(f);

%Limit to valid range of U elements.
%There is a minor bug in FFMPEG (down-sampled U and V may exceed valid range).
refU2 = max(min(refU2, 240), 16);

%Difference exclude first column and last column (FFMPEG treats the margins different than MATLAB)
%Remark: There are minor differences due to rounding (I guess).
disp(['FFMPEG Cubic interpolation with Anti-Aliasing max diff = ' num2str(max(max(abs(double(U2(:, 2:end-1)) - double(refU2(:, 2:end-1))))))]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

不同类型的下采样方法示例。
线性插值与使用抗混叠滤波器的立方插值：
在第一个例子（mandrill）中没有明显的差异在第二个例子（圆形和矩形）中，存在微小的可见差异第三个例子（行）演示了锯齿伪影备注：显示图像，使用立方插值从YUV422到YUV444进行上采样，并从YUV444转换为RGB。

线性插值与具有抗锯齿的立方体（mandrill）：
线性插值与带抗锯齿的立方体（圆和矩形）：
线性插值与具有抗锯齿的Cubic（演示混叠伪像）：

转换YUV4：4：4到YUV4：2：2图像

1 个答案: