个人博客 - 刘佳华

CUDA NV12 缩放图像

发表于 分类于

最近邻插值法与双线性差值法缩放图像

简介

通常处理图像时经常会需要缩放图像，FFmpeg 可以使用 SwsContext 方法缩放图像，OpenCV 可以使用 cv::resize 方法缩放图像，但是用这些方法缩放的同时会消耗更多时间，在选择使用 CUDA 处理图像的情况下，就直接一起缩放图像。
图像缩放有多重算法，最常见的是：

  • 最近邻插值法：速度最快，但是有时可以看到明显锯齿。
  • 双线性差值法：使用最多的方式，使用邻近四个点来计算像素值。
  • 双三次插值法（不实现该方法）：速度较慢，使用邻近十六个点来计算像素值。

实现

代码以 YUV NV12 图片格式编写，其他图片类型带入公式即可。

最近邻插值法

计算方法

计算图片缩放百分比，得到一个在原图像的百分比坐标吗，用当前坐标乘以百分比获取原图像像素值，通常为小数，删除小数部分取整使用左上角像素坐标，也可以选择四舍五入的方式，或者 +0.5 或 -0.5 后取整，作为缩放图像的像素值。

1
2
int fx = outX * (inWidth / outWidth);
int fy = outY * (inHeight / outHeight);

代码
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
/// <summary>
/// 缩放图像核函数 YUV420P
/// 最近邻插值
/// </summary>
/// <param name="pInYData">输入图片 YUV YUV420P Y</param>
/// <param name="pInUData">输入图片 YUV YUV420P U</param>
/// <param name="pInVData">输入图片 YUV YUV420P V</param>
/// <param name="pInWidth">输入图片宽度</param>
/// <param name="pInHeight">输入图像高度</param>
/// <param name="pOutYData">输出图片 YUV YUV420P Y</param>
/// <param name="pOutUData">输出图片 YUV YUV420P U</param>
/// <param name="pOutVData">输出图片 YUV YUV420P V</param>
/// <param name="pOutWidth">输出图片宽度</param>
/// <param name="pOutHeight">输出图像高度</param>
/// <returns>缩放后图像</returns>
__global__ void ReSizeKernel_Nearest_YUV420P(unsigned char* pInYData, unsigned char* pInUData, unsigned char* pInVData, int pInWidth, int pInHeight,
	unsigned char* pOutYData, unsigned char* pOutUData, unsigned char* pOutVData, int pOutWidth, int pOutHeight)
{
	int tidx = threadIdx.x + blockDim.x * blockIdx.x;
	int tidy = threadIdx.y + blockDim.y * blockIdx.y;

	if (tidx < pOutWidth && tidy < pOutHeight)
	{
		int srcX = tidx * ((float)(pInWidth - 1) / (pOutWidth - 1));
		int srcY = tidy * ((float)(pInHeight - 1) / (pOutHeight - 1));

		int idx_in_y = srcY * pInWidth + srcX;
		int idx_in_uv = srcY / 2 * pInWidth / 2 + srcX / 2;

		int idx_out_y = tidy * pOutWidth + tidx;
		int idx_out_uv = tidy / 2 * pOutWidth / 2 + tidx / 2;

		// Y
		pOutYData[idx_out_y] = pInYData[idx_in_y];
		// U
		pOutUData[idx_out_uv] = pInUData[idx_in_uv];
		// V
		pOutVData[idx_out_uv] = pInVData[idx_in_uv];
	}
}

/// <summary>
/// 修改大小 最近邻插值 YUV420P
/// </summary>
/// <param name="frame">输入图像</param>
/// <param name="width">修改宽度</param>
/// <param name="height">修改高度</param>
/// <returns>修改后图像</returns>
AVFrame* ReSize_Nearest_YUV420P(AVFrame* frame, int width, int height)
{
	auto img_size_y = width * height * sizeof(unsigned char);
	auto img_size_uv = (width / 2) * (height / 2) * sizeof(unsigned char);

	AVFrame* dstImg;
	unsigned char* outputY = nullptr;
	unsigned char* outputU = nullptr;
	unsigned char* outputV = nullptr;

	dstImg = av_frame_alloc();
	av_image_alloc(dstImg->data, dstImg->linesize, width, height, (AVPixelFormat)frame->format, 1);
	dstImg->width = width;
	dstImg->height = height;
	dstImg->format = (AVPixelFormat)frame->format;

	cudaMalloc(&outputY, img_size_y);
	cudaMalloc(&outputU, img_size_uv);
	cudaMalloc(&outputV, img_size_uv);

	dim3 block(32, 32);
	dim3 grid((width + block.x - 1) / block.x, (height + block.y - 1) / block.y);
	ReSizeKernel_Nearest_YUV420P << <grid, block >> > (frame->data[0], frame->data[1], frame->data[2], frame->width, frame->height, outputY, outputU, outputV, width, height);
	cudaThreadSynchronize();

	// 图像从 Gpu 拷贝到 Cpu
	cudaMemcpy(dstImg->data[0], outputY, img_size_y, cudaMemcpyDeviceToHost);
	cudaMemcpy(dstImg->data[1], outputU, img_size_uv, cudaMemcpyDeviceToHost);
	cudaMemcpy(dstImg->data[2], outputV, img_size_uv, cudaMemcpyDeviceToHost);
	return dstImg;
}

/// <summary>
/// 缩放图像核函数 NV12
/// 最近邻插值
/// </summary>
/// <param name="pInYData">输入图片 YUV NV12 Y</param>
/// <param name="pInUVData">输入图片 YUV NV12 UV</param>
/// <param name="pInWidth">输入图片宽度</param>
/// <param name="pInHeight">输入图像高度</param>
/// <param name="pOutYData">输出图片 YUV NV12 Y</param>
/// <param name="pOutUVData">输出图片 YUV NV12 UV</param>
/// <param name="pOutWidth">输出图片宽度</param>
/// <param name="pOutHeight">输出图像高度</param>
/// <returns>缩放后图像</returns>
__global__ void ReSizeKernel_Nearest_NV12(unsigned char* pInYData, unsigned char* pInUVData, int pInWidth, int pInHeight,
	unsigned char* pOutYData, unsigned char* pOutUVData, int pOutWidth, int pOutHeight)
{
	int tidx = threadIdx.x + blockDim.x * blockIdx.x;
	int tidy = threadIdx.y + blockDim.y * blockIdx.y;

	if (tidx < pOutWidth && tidy < pOutHeight)
	{
		int srcX = tidx * ((float)(pInWidth - 1) / (pOutWidth - 1));
		int srcY = tidy * ((float)(pInHeight - 1) / (pOutHeight - 1));

		int idx_in_y = srcY * pInWidth + srcX;
		int idx_in_uv = srcY / 2 * pInWidth + srcX;

		int idx_out_y = tidy * pOutWidth + tidx;
		int idx_out_uv = tidy / 2 * pOutWidth + tidx;

		// Y
		pOutYData[idx_out_y] = pInYData[idx_in_y];
		// U
		pOutUVData[tidx % 2 == 0 ? idx_out_uv : idx_out_uv - 1] = pInUVData[srcX % 2 == 0 ? idx_in_uv : idx_in_uv - 1];
		// V
		pOutUVData[tidx % 2 == 0 ? idx_out_uv + 1 : idx_out_uv] = pInUVData[srcX % 2 == 0 ? idx_in_uv + 1 : idx_in_uv];
	}
}

/// <summary>
/// 修改大小 最近邻插值 NV12
/// </summary>
/// <param name="frame">输入图像</param>
/// <param name="width">修改宽度</param>
/// <param name="height">修改高度</param>
/// <returns>修改后图像</returns>
AVFrame* ReSize_Nearest_NV12(AVFrame* frame, int width, int height)
{
	auto img_size_y = width * height * sizeof(unsigned char);
	auto img_size_uv = width * (height / 2) * sizeof(unsigned char);

	AVFrame* dstImg;
	unsigned char* outputY = nullptr;
	unsigned char* outputUV = nullptr;

	dstImg = av_frame_alloc();
	av_image_alloc(dstImg->data, dstImg->linesize, width, height, (AVPixelFormat)frame->format, 1);
	dstImg->width = width;
	dstImg->height = height;
	dstImg->format = (AVPixelFormat)frame->format;

	cudaMalloc(&outputY, img_size_y);
	cudaMalloc(&outputUV, img_size_uv);

	dim3 block(32, 32);
	dim3 grid((width + block.x - 1) / block.x, (height + block.y - 1) / block.y);
	ReSizeKernel_Nearest_NV12 << <grid, block >> > (frame->data[0], frame->data[1], frame->width, frame->height, outputY, outputUV, width, height);
	cudaThreadSynchronize();

	// 图像从 Gpu 拷贝到 Cpu
	cudaMemcpy(dstImg->data[0], outputY, img_size_y, cudaMemcpyDeviceToHost);
	cudaMemcpy(dstImg->data[1], outputUV, img_size_uv, cudaMemcpyDeviceToHost);
	return dstImg;
}

双线性差值法

计算方法

与最近邻插值法一样，先计算图片缩放百分比，得到一个在原图像的百分比坐标吗，用当前坐标乘以百分比获取原图像像素值，通常为小数，取小数相邻的两个像素，比如计算像素坐标为 7.5，则取删除小数的 7 和填充小数的 8 作为相邻的两个值，放在图像坐标中上下左右相邻四个像素作为计算数据。

  1. 获取在原图像的百分比像素，由于数据数组通常以 0 开始，宽度高度减 1 后计算更精准。

    1
    2
    float fx = outX * ((float)(inWidth - 1) / (outWidth - 1));
    float fy = outY * ((float)(inHeight - 1) / (outHeight - 1));

  2. 取相邻四个像素坐标，直接取整为左上角坐标，如果还有余数的情况下 +1 为右下角坐标。

    1
    2
    3
    4
    int fx0 = fx;
    int fy0 = fy;
    int fx1 = fx > fx0 ? fx0 + 1 : fx0;
    int fy1 = fy > fy0 ? fy0 + 1 : fy0;

  3. 取小数部分作为四个像素计算的分量

    1
    2
    float xProportion = fx - fx0;
    float yProportion = fy - fy0;

  4. 带入公式计算新像素值

    1
    f(x,y) = f(0,0)(1-x)(1-y) + f(1,0)x(1-y) + f(0,1)(1-x)y + f(1,1)xy;
代码
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
/// <summary>
/// 缩放图像核函数 YUV420P
/// 双线性差值
/// f(x,y) = f(0,0)(1-x)(1-y) + f(1,0)x(1-y) + f(0,1)(1-x)y + f(1,1)xy;
/// </summary>
/// <param name="pInYData">输入图片 YUV YUV420P Y</param>
/// <param name="pInUData">输入图片 YUV YUV420P U</param>
/// <param name="pInVData">输入图片 YUV YUV420P V</param>
/// <param name="pInWidth">输入图片宽度</param>
/// <param name="pInHeight">输入图像高度</param>
/// <param name="pOutYData">输出图片 YUV YUV420P Y</param>
/// <param name="pOutUData">输出图片 YUV YUV420P U</param>
/// <param name="pOutVData">输出图片 YUV YUV420P V</param>
/// <param name="pOutWidth">输出图片宽度</param>
/// <param name="pOutHeight">输出图像高度</param>
/// <returns>缩放后图像</returns>
__global__ void ReSizeKernel_Bilinear_YUV420P(unsigned char* pInYData, unsigned char* pInUData, unsigned char* pInVData, int pInWidth, int pInHeight,
	unsigned char* pOutYData, unsigned char* pOutUData, unsigned char* pOutVData, int pOutWidth, int pOutHeight)
{
	int tidx = threadIdx.x + blockDim.x * blockIdx.x;
	int tidy = threadIdx.y + blockDim.y * blockIdx.y;

	if (tidx < pOutWidth&& tidy < pOutHeight)
	{
		float srcX = tidx * ((float)(pInWidth - 1) / (pOutWidth - 1));
		float srcY = tidy * ((float)(pInHeight - 1) / (pOutHeight - 1));

		// 计算取图像坐标
		int fx0 = srcX;
		int fy0 = srcY;
		int fx1 = srcX > fx0 ? fx0 + 1 : fx0;
		int fy1 = srcY > fy0 ? fy0 + 1 : fy0;

		// 计算取像素比例
		float xProportion = srcX - fx0;
		float yProportion = srcY - fy0;

		// 四个输入坐标
		int idx_in_y_00 = fy0 * pInWidth + fx0;
		int idx_in_uv_00 = fy0 / 2 * pInWidth / 2 + fx0 / 2;

		int idx_in_y_10 = fy1 * pInWidth + fx0;
		int idx_in_uv_10 = fy1 / 2 * pInWidth / 2 + fx0 / 2;

		int idx_in_y_01 = fy0 * pInWidth + fx1;
		int idx_in_uv_01 = fy0 / 2 * pInWidth / 2 + fx1 / 2;

		int idx_in_y_11 = fy1 * pInWidth + fx1;
		int idx_in_uv_11 = fy1 / 2 * pInWidth / 2 + fx1 / 2;

		// 输出坐标
		int idx_out_y = tidy * pOutWidth + tidx;
		int idx_out_uv = tidy / 2 * pOutWidth / 2 + tidx / 2;

		// Y
		pOutYData[idx_out_y] =
			pInYData[idx_in_y_00] * (1 - xProportion) * (1 - yProportion) +
			pInYData[idx_in_y_10] * xProportion * (1 - yProportion) +
			pInYData[idx_in_y_01] * (1 - xProportion) * yProportion +
			pInYData[idx_in_y_11] * xProportion * yProportion;

		// U
		pOutUData[idx_out_uv] =
			pInUData[idx_in_uv_00] * (1 - xProportion) * (1 - yProportion) +
			pInUData[idx_in_uv_10] * xProportion * (1 - yProportion) +
			pInUData[idx_in_uv_01] * (1 - xProportion) * yProportion +
			pInUData[idx_in_uv_11] * xProportion * yProportion;

		// V
		pOutVData[idx_out_uv] =
			pInVData[idx_in_uv_00] * (1 - xProportion) * (1 - yProportion) +
			pInVData[idx_in_uv_10] * xProportion * (1 - yProportion) +
			pInVData[idx_in_uv_01] * (1 - xProportion) * yProportion +
			pInVData[idx_in_uv_11] * xProportion * yProportion;
	}
}

/// <summary>
/// 修改大小 双线性差值 YUV420P
/// </summary>
/// <param name="frame">输入图像</param>
/// <param name="width">修改宽度</param>
/// <param name="height">修改高度</param>
/// <returns>修改后图像</returns>
AVFrame* ReSize_Bilinear_YUV420P(AVFrame* frame, int width, int height)
{
	auto img_size_y = width * height * sizeof(unsigned char);
	auto img_size_uv = (width / 2) * (height / 2) * sizeof(unsigned char);

	AVFrame* dstImg;
	unsigned char* outputY = nullptr;
	unsigned char* outputU = nullptr;
	unsigned char* outputV = nullptr;

	dstImg = av_frame_alloc();
	av_image_alloc(dstImg->data, dstImg->linesize, width, height, (AVPixelFormat)frame->format, 1);
	dstImg->width = width;
	dstImg->height = height;
	dstImg->format = (AVPixelFormat)frame->format;

	cudaMalloc(&outputY, img_size_y);
	cudaMalloc(&outputU, img_size_uv);
	cudaMalloc(&outputV, img_size_uv);

	dim3 block(32, 32);
	dim3 grid((width + block.x - 1) / block.x, (height + block.y - 1) / block.y);
	ReSizeKernel_Bilinear_YUV420P << <grid, block >> > (frame->data[0], frame->data[1], frame->data[2], frame->width, frame->height, outputY, outputU, outputV, width, height);
	cudaThreadSynchronize();

	// 图像从 Gpu 拷贝到 Cpu
	cudaMemcpy(dstImg->data[0], outputY, img_size_y, cudaMemcpyDeviceToHost);
	cudaMemcpy(dstImg->data[1], outputU, img_size_uv, cudaMemcpyDeviceToHost);
	cudaMemcpy(dstImg->data[2], outputV, img_size_uv, cudaMemcpyDeviceToHost);
	return dstImg;
}

/// <summary>
/// 缩放图像核函数 NV12
/// 双线性差值
/// f(x,y) = f(0,0)(1-x)(1-y) + f(1,0)x(1-y) + f(0,1)(1-x)y + f(1,1)xy;
/// </summary>
/// <param name="pInYData">输入图片 YUV NV12 Y</param>
/// <param name="pInUVData">输入图片 YUV NV12 UV</param>
/// <param name="pInWidth">输入图片宽度</param>
/// <param name="pInHeight">输入图像高度</param>
/// <param name="pOutYData">输出图片 YUV NV12 Y</param>
/// <param name="pOutUVData">输出图片 YUV NV12 UV</param>
/// <param name="pOutWidth">输出图片宽度</param>
/// <param name="pOutHeight">输出图像高度</param>
/// <returns>缩放后图像</returns>
__global__ void ReSizeKernel_Bilinear_NV12(unsigned char* pInYData, unsigned char* pInUVData, int pInWidth, int pInHeight,
	unsigned char* pOutYData, unsigned char* pOutUVData, int pOutWidth, int pOutHeight)
{
	int tidx = threadIdx.x + blockDim.x * blockIdx.x;
	int tidy = threadIdx.y + blockDim.y * blockIdx.y;

	if (tidx < pOutWidth&& tidy < pOutHeight)
	{
		float srcX = tidx * ((float)(pInWidth - 1) / (pOutWidth - 1));
		float srcY = tidy * ((float)(pInHeight - 1) / (pOutHeight - 1));

		// 计算取图像坐标
		int fx0 = srcX;
		int fy0 = srcY;
		int fx1 = srcX > fx0 ? fx0 + 1 : fx0;
		int fy1 = srcY > fy0 ? fy0 + 1 : fy0;

		// 计算取像素比例
		float xProportion = srcX - fx0;
		float yProportion = srcY - fy0;

		// 四个输入坐标
		int idx_in_y_00 = fy0 * pInWidth + fx0;
		int idx_in_uv_00 = fy0 / 2 * pInWidth + fx0;

		int idx_in_y_10 = fy1 * pInWidth + fx0;
		int idx_in_uv_10 = fy1 / 2 * pInWidth + fx0;

		int idx_in_y_01 = fy0 * pInWidth + fx1;
		int idx_in_uv_01 = fy0 / 2 * pInWidth + fx1;

		int idx_in_y_11 = fy1 * pInWidth + fx1;
		int idx_in_uv_11 = fy1 / 2 * pInWidth + fx1;

		// 输出坐标
		int idx_out_y = tidy * pOutWidth + tidx;
		int idx_out_uv = tidy / 2 * pOutWidth + tidx;

		// Y
		pOutYData[idx_out_y] =
			pInYData[idx_in_y_00] * (1 - xProportion) * (1 - yProportion) +
			pInYData[idx_in_y_10] * xProportion * (1 - yProportion) +
			pInYData[idx_in_y_01] * (1 - xProportion) * yProportion +
			pInYData[idx_in_y_11] * xProportion * yProportion;

		// U
		pOutUVData[tidx % 2 == 0 ? idx_out_uv : idx_out_uv - 1] =
			pInUVData[fx0 % 2 == 0 ? idx_in_uv_00 : idx_in_uv_00 - 1] * (1 - xProportion) * (1 - yProportion) +
			pInUVData[fx0 % 2 == 0 ? idx_in_uv_10 : idx_in_uv_10 - 1] * xProportion * (1 - yProportion) +
			pInUVData[fx1 % 2 == 0 ? idx_in_uv_01 : idx_in_uv_01 - 1] * (1 - xProportion) * yProportion +
			pInUVData[fx1 % 2 == 0 ? idx_in_uv_11 : idx_in_uv_11 - 1] * xProportion * yProportion;

		// V
		pOutUVData[tidx % 2 == 0 ? idx_out_uv + 1 : idx_out_uv] =
			pInUVData[fx0 % 2 == 0 ? idx_in_uv_00 + 1 : idx_in_uv_00] * (1 - xProportion) * (1 - yProportion) +
			pInUVData[fx0 % 2 == 0 ? idx_in_uv_10 + 1 : idx_in_uv_10] * xProportion * (1 - yProportion) +
			pInUVData[fx1 % 2 == 0 ? idx_in_uv_01 + 1 : idx_in_uv_01] * (1 - xProportion) * yProportion +
			pInUVData[fx1 % 2 == 0 ? idx_in_uv_11 + 1 : idx_in_uv_11] * xProportion * yProportion;
	}
}

/// <summary>
/// 修改大小 双线性差值 NV12
/// </summary>
/// <param name="frame">输入图像</param>
/// <param name="width">修改宽度</param>
/// <param name="height">修改高度</param>
/// <returns>修改后图像</returns>
AVFrame* ReSize_Bilinear_NV12(AVFrame* frame, int width, int height)
{
	auto img_size_y = width * height * sizeof(unsigned char);
	auto img_size_uv = width * (height / 2) * sizeof(unsigned char);

	AVFrame* dstImg;
	unsigned char* outputY = nullptr;
	unsigned char* outputUV = nullptr;

	dstImg = av_frame_alloc();
	av_image_alloc(dstImg->data, dstImg->linesize, width, height, (AVPixelFormat)frame->format, 1);
	dstImg->width = width;
	dstImg->height = height;
	dstImg->format = (AVPixelFormat)frame->format;

	cudaMalloc(&outputY, img_size_y);
	cudaMalloc(&outputUV, img_size_uv);

	dim3 block(32, 32);
	dim3 grid((width + block.x - 1) / block.x, (height + block.y - 1) / block.y);
	ReSizeKernel_Bilinear_NV12 << <grid, block >> > (frame->data[0], frame->data[1], frame->width, frame->height, outputY, outputUV, width, height);
	cudaThreadSynchronize();

	// 图像从 Gpu 拷贝到 Cpu
	cudaMemcpy(dstImg->data[0], outputY, img_size_y, cudaMemcpyDeviceToHost);
	cudaMemcpy(dstImg->data[1], outputUV, img_size_uv, cudaMemcpyDeviceToHost);
	return dstImg;
}

外部调用方法

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
/// <summary>
/// 修改图像大小
/// </summary>
/// <param name="frame">输入图像</param>
/// <param name="width">修改宽度</param>
/// <param name="height">修改高度</param>
/// <param name="type">0:最近邻插值 1:双线性差值</param>
/// <returns>修改后图像</returns>
extern "C" AVFrame * ReSize(AVFrame * frame, int width, int height, int type)
{
	AVFrame* outFrame;

	switch (frame->format)
	{
	case AV_PIX_FMT_YUV420P:
		if (type == 0)
		{
			outFrame = ReSize_Nearest_YUV420P(frame, width, height);
		}
		else if (type == 1)
		{
			outFrame = ReSize_Bilinear_YUV420P(frame, width, height);
		}
		break;
	case AV_PIX_FMT_NV12:
		if (type == 0)
		{
			outFrame = ReSize_Nearest_NV12(frame, width, height);
		}
		else if (type == 1)
		{
			outFrame = ReSize_Bilinear_NV12(frame, width, height);
		}
		break;
	default:
		break;
	}

	return outFrame;
}