Optimización H.266/VVC: Compresión de Video Avanzada | Consulting AI

Aceleración con TensorRT en RTX 4090

Implementación paso a paso para optimizar la compresión en H.266/VVC:

1. Preparación de Datos

• Descargar VVC Test Sequences:
  • Extraer características con OpenCV:

    import cv2
    import numpy as np
    
    def extract_features(ctu):
        # Gradientes Sobel
        sobel_x = cv2.Sobel(ctu, cv2.CV_32F, 1, 0, ksize=3)
        sobel_y = cv2.Sobel(ctu, cv2.CV_32F, 0, 1, ksize=3)
        magnitude = np.sqrt(sobel_x**2 + sobel_y**2)
        # Normalizar
        magnitude = cv2.normalize(magnitude, None, 0, 1, cv2.NORM_MINMAX)
        # Histograma (16 bins)
        hist = cv2.calcHist([ctu], [0], None, [16], [0, 256])
        hist = cv2.normalize(hist, hist, 0, 1, cv2.NORM_MINMAX)
        # Resize a 128x128
        resized = cv2.resize(magnitude, (128, 128), interpolation=cv2.INTER_AREA)
        return np.concatenate([resized.flatten(), hist.flatten()])
    

  • Generar etiquetas: Desde VTM con --DumpSplitDecisions.
  • Almacenamiento: En formato HDF5 para mayor eficiencia.

  2. Diseño del Modelo

  import torch
  import torch.nn as nn
  
  class PartitionTransformer(nn.Module):
      def __init__(self, input_dim=128*128+16, num_heads=8, num_layers=8):
          super().__init__()
          self.embedding = nn.Linear(input_dim, 384)
          self.transformer = nn.TransformerEncoder(
              nn.TransformerEncoderLayer(d_model=384, nhead=num_heads, dim_feedforward=1536, dropout=0.1),
              num_layers=num_layers
          )
          self.fc = nn.Linear(384, 6)  # 6 tipos de particiones
  
      def forward(self, x):
          x = self.embedding(x)
          x = self.transformer(x)
          return torch.softmax(self.fc(x), dim=-1)
  

  3. Entrenamiento

  • Dataset: VVC Test Sequences + DIV2K upsampled.
  • Pérdida: CrossEntropyLoss + regularización L2 (λ=0.01).
  • Optimizador: AdamW (lr=1e-4, weight_decay=0.01).
  • Estrategia:
    • Entrenamiento supervisado inicial con decisiones óptimas de VTM.
    • Fine-tuning con Proximal Policy Optimization (PPO) para optimización end-to-end.
  • Hardware: 2x NVIDIA A100 (40 GB), 50 épocas, batch_size=32.
  • Código:

    model = PartitionTransformer().cuda()
    optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=0.01)
    criterion = nn.CrossEntropyLoss()
    
    for epoch in range(50):
        for batch in dataloader:
            features, labels = batch
            features, labels = features.cuda(), labels.cuda()
            preds = model(features)
            loss = criterion(preds, labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
    
    torch.jit.save(torch.jit.script(model), "partition_model.pt")
    

  4. Integración en VTM

  • Modificar EncCu.cpp en VTM 12.0:

    #include <torch/script.h>
    #include <opencv2/opencv.hpp>
    #include <xxhash.h>
    
    class PartitionPredictor {
    private:
        torch::jit::script::Module model;
        float confidenceThreshold = 0.7;
        std::unordered_map<uint64_t, std::vector<float>> predictionCache;
    
        uint64_t hashCTUFeatures(const std::vector<float>& features) {
            return XXH64(features.data(), features.size() * sizeof(float), 0);
        }
    
        std::vector<float> extractFeatures(const CodingUnit& cu) {
            cv::Mat lumaBlock(cu.blocks[COMPONENT_Y].height + 8, cu.blocks[COMPONENT_Y].width + 8, CV_8UC1);
            for (int y = -4; y < cu.blocks[COMPONENT_Y].height + 4; y++) {
                for (int x = -4; x < cu.blocks[COMPONENT_Y].width + 4; x++) {
                    int srcX = std::clamp(x + cu.blocks[COMPONENT_Y].x, 0, cu.cs->picture->lwidth() - 1);
                    int srcY = std::clamp(y + cu.blocks[COMPONENT_Y].y, 0, cu.cs->picture->lheight() - 1);
                    lumaBlock.at<uint8_t>(y + 4, x + 4) = cu.cs->picture->getRecoBuf().Y().at(srcX, srcY);
                }
            }
    
            cv::Mat sobelX, sobelY, magnitude;
            cv2::Sobel(lumaBlock, sobelX, CV_32F, 1, 0, 3);
            cv2::Sobel(lumaBlock, sobelY, CV_32F, 0, 1, 3);
            cv2::magnitude(sobelX, sobelY, magnitude);
    
            cv::Mat normalized;
            cv2::normalize(magnitude, normalized, 0, 1, cv2::NORM_MINMAX);
    
            cv::Mat hist;
            int histSize[] = {16};
            float range[] = {0, 256};
            const float* histRange[] = {range};
            cv2::calcHist(&lumaBlock, 1, 0, cv::Mat(), hist, 1, histSize, histRange);
            cv2::normalize(hist, hist, 0, 1, cv2::NORM_MINMAX);
    
            cv::Mat resizedFeatures;
            cv2::resize(normalized, resizedFeatures, cv2::Size(128, 128), 0, 0, cv2::INTER_AREA);
    
            std::vector<float> features;
            features.reserve(128*128 + 16);
            for (int y = 0; y < 128; y++) {
                for (int x = 0; x < 128; x++) {
                    features.push_back(resizedFeatures.at<float>(y, x));
                }
            }
            for (int i = 0; i < 16; i++) {
                features.push_back(hist.at<float>(i));
            }
            return features;
        }
    
    public:
        PartitionPredictor(const std::string& modelPath) {
            model = torch::jit::load(modelPath);
            model.eval();
            if (torch::cuda::is_available()) model.to(torch::kCUDA);
        }
    
        std::vector<float> predictPartitions(const CodingUnit& cu) {
            auto features = extractFeatures(cu);
            auto featureHash = hashCTUFeatures(features);
            if (auto it = predictionCache.find(featureHash); it != predictionCache.end()) {
                return it->second;
            }
    
            torch::Tensor input = torch::from_blob(features.data(), {1, 1, 128*128 + 16}, torch::kFloat32);
            if (torch::cuda::is_available()) input = input.to(torch::kCUDA);
    
            torch::NoGradGuard no_grad;
            auto output = model.forward({input}).toTensor().to(torch::kCPU);
            std::vector<float> predictions(output.data_ptr<float>(), output.data_ptr<float>() + output.numel());
            predictionCache[featureHash] = predictions;
            return predictions;
        }
    
        bool shouldEvaluatePartition(PartitionType partType, const std::vector<float>& predictions) {
            if (predictions.empty()) return true;
            int partIndex;
            switch (partType) {
    

  Este es solo el inicio de la implementación para optimizar la compresión en H.266/VVC…