CUDA Kernels: Video Processing Algorithms Explained

>>(d_rgb_frame, d_gray_frame, width, height); Bilinear Scaling Kernel Bilinear interpolation kernels resize images by computing each output pixel value from four neighboring input pixels. This provides smoother results than nearest-neighbor scaling and is suitable for both upscaling and downscaling. Implementing bilinear scaling as a CUDA kernel allows high-resolution frames to be resized efficiently, maintaining real-time throughput CUDA Kernel Structure (Pseudocode) __global__ void scale_bilinear(const uint8_t* input, uint8_t* output, int in_w, int in_h, int out_w, int out_h) { // Compute target pixel coordinates // Map back to source space and apply bilinear interpolation } This approach is more accurate than nearest-neighbor and suitable for quality-preserving downscaling or upscaling. Brightness Adjustment Kernel Brightness adjustment modifies each pixel’s intensity by adding or subtracting a scalar value from all color channels. The CUDA kernel processes all pixels in parallel, making it ideal for color correction or normalization in video pipelines. CUDA Kernel CUDA kernels are device-side GPU functions launched from the host. Each kernel executes in parallel across threads and blocks, performing operations like filtering, transformation, or analysis on individual pixels or pixel groups in a video frame. __global__ void adjust_brightness(uint8_t* frame, int width, int height, int pitch, int value) { int x = blockIdx.x * blockDim.x + threadIdx.x; int y = blockIdx.y * blockDim.y + threadIdx.y; if (x >= width || y >= height) return; int idx = y * pitch + x * 3; for (int c = 0; c < 3; ++c) { int pixel = frame[idx + c] + value; frame[idx + c] = min(255, max(0, pixel)); } } Frame Differencing (Motion Detection) Frame differencing detects motion by subtracting pixel values of consecutive frames and applying a threshold. The CUDA kernel compares corresponding pixels in parallel, quickly generating a binary mask indicating regions of motion. This is for surveillance, tracking, and event detection in video streams Kernel Implementation __global__ void frame_difference(uint8_t* frame1, uint8_t* frame2, uint8_t* output, int width, int height) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < width * height) { int diff = abs(frame1[idx] - frame2[idx]); output[idx] = diff > 25 ? 255 : 0; } } Gaussian Blur Using Shared Memory Gaussian blur smooths images by averaging neighboring pixels with a weighted kernel, reducing noise and detail. Leveraging shared memory in the CUDA kernel minimizes global memory access, significantly improving performance. This method is widely used for preprocessing, edge detection, and noise reduction in video analytics Kernel Overview __global__ void gaussian_blur(uint8_t* input, uint8_t* output, int width, int height, float* kernel) { __shared__ uint8_t tile[32][32]; // Add padding as needed int x = blockIdx.x * blockDim.x + threadIdx.x - 2; int y = blockIdx.y * blockDim.y + threadIdx.y - 2; if (x >= 0 && x < width && y >= 0 && y < height) { tile[threadIdx.y][threadIdx.x] = input[y * width + x]; } __syncthreads(); if (threadIdx.x >= 2 && threadIdx.x < blockDim.x - 2 && threadIdx.y >= 2 && threadIdx.y < blockDim.y - 2) { float val = 0.0f; for (int ky = 0; ky < 5; ky++) { for (int kx = 0; kx < 5; kx++) { val += tile[threadIdx.y - 2 + ky][threadIdx.x - 2 + kx] * kernel[ky * 5 + kx]; } } output[(y + 2) * width + (x + 2)] = val; } }

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