将 YUV4:4:4 转换为 YUV4:2:2 图片
Convert YUV4:4:4 to YUV4:2:2 images
互联网上有很多关于 YUV4:4:4 和 YUV4:2:2 格式之间差异的信息，但是，我找不到任何说明如何将 YUV4:4:4 转换为YUV4:2:2。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了它，并且可以指导我找到描述转换算法的来源。当然，拥有软件代码固然很好，但是能够接触到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在转换过程中如何管理字节。
我发现了几个类似的问题this and this, however, could not get my question answered. Also, I posted this question on the Photography forum，他们认为这是一个软件问题。
之所以找不到具体的描述，是因为有很多方法可以做到。
让我们从维基百科开始：https://en.wikipedia.org/wiki/Chroma_subsampling#4:2:2
4:4:4:
Each of the three Y'CbCr components have the same sample rate, thus there is no chroma subsampling. This scheme is sometimes used in high-end film scanners and cinematic post production.
和
4:2:2:
The two chroma components are sampled at half the sample rate of luma: the horizontal chroma resolution is halved. This reduces the bandwidth of an uncompressed video signal by one-third with little to no visual difference.
注意：术语 YCbCr 和 YUV 可以互换使用。
https://en.wikipedia.org/wiki/YCbCr
Y′CbCr is often confused with the YUV color space, and typically the terms YCbCr and YUV are used interchangeably, leading to some confusion; when referring to signals in video or digital form, the term "YUV" mostly means "Y′CbCr".
数据内存排序：
同样，格式不止一种。
英特尔 IPP 文档定义了两个主要类别："Pixel-Order Image Formats" 和 "Planar Image Formats"。
这里有一个很好的文档：https://software.intel.com/en-us/node/503876
参考这里：http://www.fourcc.org/yuv.php#NV12 了解 YUV 像素排列格式。
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 了解下采样说明。
让我们假设 "Pixel-Order" 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是内存中的低字节。
上面描述的4:2:2数据顺序被命名为UYVY或YUY2像素格式。
转换算法：
"Naive sub-sampling":
"Throw"每秒U/V分量：
拿U0，扔U1，拿V0，扔V1...
资料来源：Y0 U0 V0 Y1 U1 V1 Y2 U2 V2
目的地：Y0U0Y1V0Y2U2Y3V2
我不推荐它，因为它会导致 aliasing 伪影。
每个 U/V 对的平均值：
采用目标 U0 等于源 (U0+U1)/2，与 V0 相同...
资料来源：Y0 U0 V0 Y1 U1 V1 Y2 U2 V2
目的地：Y0(U0+U1)/2Y1(V0+V1)/2Y2(U2+U3)/2Y3(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）。
与简单平均值相比，您通常看不出任何差异。
C 实现：
问题未标记为 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的平面表示4:2:2
平面表示可能比 "Pixel-Order" 格式更直观。
在平面表示中，每个颜色通道都表示为一个单独的矩阵，可以显示为图像。
示例：
RGB 格式的原始图像（在转换为 YUV 之前）：
YUV 4:4:4 格式的图像通道：
（左 YUV 三元组以灰度级表示，右 YUV 三元组使用伪色表示）。
YUV 4:2:2格式的图像通道（水平后Chroma subsampling）：
（左 YUV 三元组以灰度表示，右 YUV 三元组使用 "false colors" 表示）。
如您所见，在 4:2:2 格式中，U 和 V 通道在水平轴上被下采样（收缩）。
备注：
U和V通道的"false colors"表示用于强调Y是Luma channel and U and V are the Chrominance通道。
高阶插值和抗锯齿滤波器：
以下 MATLAB 代码示例显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：您无需了解 MATLAB 编程即可理解示例。
您确实需要一些有关通过 Kernel 和图像之间的卷积进行图像过滤的知识。
%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;
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 (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.
if isunix
[status, cmdout] = system(['./ffmpeg -y -s ', num2str(N), 'x', num2str(M), ' -pix_fmt yuv444p -i yuv444.yuv -pix_fmt yuv422p yuv422.yuv']);
else
[status, cmdout] = system(['ffmpeg.exe -y -s ', num2str(N), 'x', num2str(M), ' -pix_fmt yuv444p -i yuv444.yuv -pix_fmt yuv422p yuv422.yuv']);
end
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.
%In FFMPEG down-sampled U and V may exceed valid range (there is probably a way to tell FFMPEG to limit the result).
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))))))]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
不同类型下采样方法的示例。
使用抗混叠滤波器的线性插值与三次插值：
在第一个示例 (山魈) 中没有明显的差异。
在第二个示例（圆形和矩形）中，存在细微的可见差异。
第三个示例（线条）演示了混叠伪像。
备注：显示的图像使用三次插值从 YUV422 上采样到 YUV444，并从 YUV444 转换为 RGB。
具有抗锯齿功能的线性插值与三次插值 (mandrill)：
线性插值与具有抗锯齿功能的三次插值（圆形和矩形）：
具有抗锯齿功能的线性插值与三次插值（演示锯齿伪像）：
互联网上有很多关于 YUV4:4:4 和 YUV4:2:2 格式之间差异的信息，但是，我找不到任何说明如何将 YUV4:4:4 转换为YUV4:2:2。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了它，并且可以指导我找到描述转换算法的来源。当然，拥有软件代码固然很好，但是能够接触到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在转换过程中如何管理字节。
我发现了几个类似的问题this and this, however, could not get my question answered. Also, I posted this question on the Photography forum，他们认为这是一个软件问题。
之所以找不到具体的描述，是因为有很多方法可以做到。
让我们从维基百科开始：https://en.wikipedia.org/wiki/Chroma_subsampling#4:2:2
4:4:4:
Each of the three Y'CbCr components have the same sample rate, thus there is no chroma subsampling. This scheme is sometimes used in high-end film scanners and cinematic post production.
和
4:2:2:
The two chroma components are sampled at half the sample rate of luma: the horizontal chroma resolution is halved. This reduces the bandwidth of an uncompressed video signal by one-third with little to no visual difference.
注意：术语 YCbCr 和 YUV 可以互换使用。
https://en.wikipedia.org/wiki/YCbCr
Y′CbCr is often confused with the YUV color space, and typically the terms YCbCr and YUV are used interchangeably, leading to some confusion; when referring to signals in video or digital form, the term "YUV" mostly means "Y′CbCr".
数据内存排序：
同样，格式不止一种。
英特尔 IPP 文档定义了两个主要类别："Pixel-Order Image Formats" 和 "Planar Image Formats"。
这里有一个很好的文档：https://software.intel.com/en-us/node/503876
参考这里：http://www.fourcc.org/yuv.php#NV12 了解 YUV 像素排列格式。
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 了解下采样说明。
让我们假设 "Pixel-Order" 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是内存中的低字节。
上面描述的4:2:2数据顺序被命名为UYVY或YUY2像素格式。
转换算法：
"Naive sub-sampling":
"Throw"每秒U/V分量：
拿U0，扔U1，拿V0，扔V1...
资料来源：Y0U0V0Y1U1V1Y2U2V2
目的地：Y0U0Y1V0Y2U2Y3V2
我不推荐它，因为它会导致 aliasing 伪影。每个
U/V对的平均值：
采用目标U0等于源(U0+U1)/2，与V0相同...
资料来源：Y0U0V0Y1U1V1Y2U2V2
目的地：Y0(U0+U1)/2Y1(V0+V1)/2Y2(U2+U3)/2Y3(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）。
与简单平均值相比，您通常看不出任何差异。
C 实现：
问题未标记为 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的平面表示4:2:2
平面表示可能比 "Pixel-Order" 格式更直观。
在平面表示中，每个颜色通道都表示为一个单独的矩阵，可以显示为图像。
示例：
RGB 格式的原始图像（在转换为 YUV 之前）：
YUV 4:4:4 格式的图像通道：
（左 YUV 三元组以灰度级表示，右 YUV 三元组使用伪色表示）。YUV 4:2:2格式的图像通道（水平后Chroma subsampling）：
（左 YUV 三元组以灰度表示，右 YUV 三元组使用 "false colors" 表示）。
如您所见，在 4:2:2 格式中，U 和 V 通道在水平轴上被下采样（收缩）。
备注：
U和V通道的"false colors"表示用于强调Y是Luma channel and U and V are the Chrominance通道。
高阶插值和抗锯齿滤波器：
以下 MATLAB 代码示例显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：您无需了解 MATLAB 编程即可理解示例。
您确实需要一些有关通过 Kernel 和图像之间的卷积进行图像过滤的知识。
%Prepare the input:
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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.
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%Linear interpolation without Anti-Aliasing filter:
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%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.
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%Cubic interpolation without Anti-Aliasing filter:
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%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.
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%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.
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%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;
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 (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.
if isunix
[status, cmdout] = system(['./ffmpeg -y -s ', num2str(N), 'x', num2str(M), ' -pix_fmt yuv444p -i yuv444.yuv -pix_fmt yuv422p yuv422.yuv']);
else
[status, cmdout] = system(['ffmpeg.exe -y -s ', num2str(N), 'x', num2str(M), ' -pix_fmt yuv444p -i yuv444.yuv -pix_fmt yuv422p yuv422.yuv']);
end
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.
%In FFMPEG down-sampled U and V may exceed valid range (there is probably a way to tell FFMPEG to limit the result).
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))))))]);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
不同类型下采样方法的示例。
使用抗混叠滤波器的线性插值与三次插值：
在第一个示例 (山魈) 中没有明显的差异。
在第二个示例（圆形和矩形）中，存在细微的可见差异。
第三个示例（线条）演示了混叠伪像。
备注：显示的图像使用三次插值从 YUV422 上采样到 YUV444，并从 YUV444 转换为 RGB。
具有抗锯齿功能的线性插值与三次插值 (mandrill)：
线性插值与具有抗锯齿功能的三次插值（圆形和矩形）：
具有抗锯齿功能的线性插值与三次插值（演示锯齿伪像）：