# 伪代码：核心特征提取模块
class SpatioTemporalBackbone(nn.Module):
    def __init__(self, base_ch=32):
        super().__init__()
        # 空间浅层特征：2D Depthwise Separable Conv
        self.spatial_stem = nn.Sequential(
            DSConv(3, base_ch, 3, stride=2),   # 1/2
            DSConv(base_ch, base_ch, 3, stride=2)  # 1/4
        )
        # 时域建模：3D Conv (kernel=3×3×3) + Channel Attention
        self.temporal_blocks = nn.ModuleList([
            TemporalBlock(base_ch * 2**i) for i in range(3)
        ])
        # 可变形注意力：对齐运动轨迹
        self.deform_attn = DeformableAttention(base_ch * 4, num_heads=4)

mathcal{L}_{total} = lambda_1 mathcal{L}_{PLCC} + lambda_2 mathcal{L}_{Rank} + lambda_3 mathcal{L}_{Distill}

class EfficientAlignment(nn.Module):
    def __init__(self, channels, num_neighbors=3):
        super().__init__()
        self.offset_conv = nn.Sequential(
            nn.Conv2d(channels*2, channels, 3, padding=1, groups=channels),
            nn.LeakyReLU(0.1),
            nn.Conv2d(channels, 2*num_neighbors, 3, padding=1)
        )
        self.modulation_conv = nn.Conv2d(channels*2, num_neighbors, 3, padding=1)
    
    def forward(self, ref_feat, nbr_feats):
        # ref_feat: [B,C,H,W], nbr_feats: List[[B,C,H,W]] * N
        offsets, mods = [], []
        for nbr in nbr_feats:
            cat = torch.cat([ref_feat, nbr], dim=1)
            offsets.append(self.offset_conv(cat))
            mods.append(torch.sigmoid(self.modulation_conv(cat)))
        # 使用 grid_sample 完成对齐，支持 ONNX/TensorRT 导出
        aligned = [deform_sample(nbr, off, mod) for nbr, off, mod in zip(nbr_feats, offsets, mods)]
        return torch.stack(aligned, dim=1)  # [B,N,C,H,W]

mathcal{L}_{VSR} = alpha mathcal{L}_{Charbonnier} + beta mathcal{L}_{Perceptual} + gamma (1 - Q_{NR}(hat{Y}))

# 伪代码：在线质量监控与自适应码率控制
class QualityProbe:
    def __init__(self, nr_model, window=30):
        self.nr_model = nr_model.eval()
        self.window = window
        self.history = deque(maxlen=window)
    
    @torch.no_grad()
    def update(self, lr_frame, sr_frame):
        score = self.nr_model(lr_frame, sr_frame)  # 无参考评分
        self.history.append(score)
        return np.mean(self.history)
    
    def adjust_bitrate(self, current_br, target_q=0.85):
        avg_q = np.mean(self.history)
        if avg_q < target_q * 0.95:
            return min(current_br * 1.15, MAX_BR)
        elif avg_q > target_q * 1.05:
            return max(current_br * 0.9, MIN_BR)
        return current_br

# 伪代码：TensorRT Engine 构建策略
class TRTEngineBuilder:
    def __init__(self, onnx_path):
        self.builder = trt.Builder(logger)
        self.config = self.builder.create_builder_config()
        self.config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 2 << 30)  # 2GB
        
    def build_multi_profile_engine(self, resolutions=[(360,640), (720,1280), (1080,1920), (2160,3840)]):
        network = self.builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
        parser = trt.OnnxParser(network, logger)
        parser.parse_from_file(self.onnx_path)
        
        # 核心：为每个主流分辨率创建独立 Optimization Profile
        profile = self.builder.create_optimization_profile()
        for i, (h, w) in enumerate(resolutions):
            # 输入: [B, 3, H, W] + 邻域帧 [B, N, 3, H, W]
            min_shape = (1, 3, h, w)
            opt_shape = (2, 3, h, w)  # 典型并发批次
            max_shape = (4, 3, h, w)
            profile.set_shape("input_lr", min_shape, opt_shape, max_shape)
            profile.set_shape("input_nbrs", (1, 3, 3, h, w), (2, 3, 3, h, w), (4, 3, 3, h, w))
        
        self.config.add_optimization_profile(profile)
        # 启用 FP16 + BF16 混合精度（Hopper/Ada 架构）
        self.config.set_flag(trt.BuilderFlag.FP16)
        if self.builder.platform_has_fast_fp16:
            self.config.set_flag(trt.BuilderFlag.BF16)
        
        # 关键：启用时序优化（跨层融合、常量折叠）
        self.config.set_flag(trt.BuilderFlag.TIMING_CACHE)
        timing_cache = self.config.get_timing_cache()
        # 持久化 Timing Cache 到磁盘，二次构建加速 90%+
        with open("timing_cache.bin", "wb") as f:
            f.write(timing_cache.serialize())
            
        engine = self.builder.build_serialized_network(network, self.config)
        return engine

// C++ 伪代码：异构调度决策引擎
class HeteroScheduler {
    struct DevicePerfProfile {
        float latency_ms[4];      // 4 个分辨率档位
        float power_mw[4];
        bool support_int8, support_fp16, support_bf16;
        size_t shared_mem_limit;
    };
    
    std::map<std::string, DevicePerfProfile> hw_db_; // 离线 Benchmark 入库
    
public:
    enum class Target { CPU, GPU, NPU, DSP, AUTO };
    
    Target select_optimal_target(const cv::Size& input_res, float target_fps, float power_budget_mw) {
        auto& profile = hw_db_[get_soc_model()];
        int idx = get_res_index(input_res);
        
        // 约束规划：满足实时性 + 功耗上界
        std::vector<Target> candidates = {Target::NPU, Target::GPU, Target::DSP, Target::CPU};
        for (auto t : candidates) {
            float lat = profile.latency_ms[idx][(int)t];
            float pwr = profile.power_mw[idx][(int)t];
            if (lat < 1000.0f/target_fps && pwr < power_budget_mw) {
                // 兼容性二次确认
                if (check_operator_coverage(t, current_model_)) return t;
            }
        }
        return Target::CPU; // 兜底
    }
    
    // 运行时动态切换：检测热节流/电量低时自动降级
    void on_thermal_event(ThermalLevel level) {
        if (level >= ThermalLevel::THROTTLING) {
            current_target_ = downgrade_target(current_target_);
            rebuild_execution_context(); // 重建 NPU/GPU Context
        }
    }
};

min_G max_{D_{spa}, D_{tem}} mathcal{L}_{adv} = 
mathbb{E}[log D_{spa}(Y_{hr})] + mathbb{E}[log(1 - D_{spa}(G(X_{lr})))] \
+ lambda_{tem} left( mathbb{E}[log D_{tem}(Y_{hr}^{t-1:t+1})] + mathbb{E}[log(1 - D_{tem}(G(X_{lr})^{t-1:t+1}))] right)

class CurriculumScheduler:
    def __init__(self, nr_model, milestones=[50000, 150000, 300000]):
        self.nr_model = nr_model.eval()
        self.milestones = milestones
        self.phase = 0
        
    def get_loss_weights(self, global_step, batch_stats):
        # batch_stats: {'psnr':, 'tfi':, 'gsi':, 'niqe':, 'lr_lr':}
        if global_step < self.milestones[0]:
            # Phase 1: 像素保真度优先，稳定收敛
            return dict(pixel=1.0, perceptual=0.1, nr_guide=0.0, adv=0.0)
        
        elif global_step < self.milestones[1]:
            # Phase 2: 感知质量引入，NR 指标开始指导
            nr_score = batch_stats['nr_score']  # 当前 batch 平均分
            # 动态权重：分数低时加大 NR 引导，分数高时减弱防止过拟合
            nr_weight = 0.3 * (1.0 - torch.sigmoid((nr_score - 0.7) * 10))
            return dict(pixel=0.5, perceptual=0.3, nr_guide=nr_weight, adv=0.1)
        
        elif global_step < self.milestones[2]:
            # Phase 3: 对抗微调，主观质量冲刺
            # 根据具体失真类型动态调整
            tfi, gsi = batch_stats['tfi'], batch_stats['gsi']
            weights = dict(pixel=0.2, perceptual=0.2, nr_guide=0.4, adv=0.2)
            if tfi > 0.15: weights['tem_adv'] = 0.3  # 闪烁严重加强时域判别器
            if gsi > 0.12: weights['spa_adv'] = 0.3  # 鬼影严重加强空间判别器
            return weights
        
        else:
            # Phase 4: 稳定化微调，极低 LR
            return dict(pixel=0.1, perceptual=0.1, nr_guide=0.5, adv=0.0)

class DriftDetector:
    def __init__(self, reference_window_days=7, detection_window_hours=1):
        self.ref_stats = load_historical_stats(reference_window_days) # 离线计算分位数、矩
        self.det_buffer = RingBuffer(capacity=3600) # 1h 滑动窗口
        
    def update(self, frame_features: dict): # features: nr_score, entropy, color_hist, motion_mag
        self.det_buffer.append(frame_features)
        
    def check_drift(self) -> DriftReport:
        if len(self.det_buffer) < 1000: return DriftReport(status="WARMUP")
        
        curr_dist = estimate_distribution(self.det_buffer)
        ref_dist = self.ref_stats
        
        # 1. 人口稳定性指数 (PSI) - 关注评分分布整体偏移
        psi = calculate_psi(ref_dist['nr_score'], curr_dist['nr_score'])
        
        # 2. 最大均值差异 (MMD) - 关注高维特征分布偏移 (RBF Kernel)
        mmd = calculate_mmd(ref_dist['feat_embed'], curr_dist['feat_embed'])
        
        # 3. 概念漂移检测 (ADWIN) - 关注评分与真实用户反馈的相关性断裂
        # 需要少量有标签数据或显式反馈信号
        concept_drift = self.adwin_detector.update(curr_dist['nr_score'], user_feedback_signal)
        
        severity = "NONE"
        if psi > 0.2 or mmd > 0.15 or concept_drift:
            severity = "HIGH" if (psi > 0.5 or mmd > 0.3) else "MEDIUM"
            
        return DriftReport(
            severity=severity, psi=psi, mmd=mmd, 
            top_drift_features=self.attribute_drift(curr_dist, ref_dist),
            suggested_action=self.recommend_action(severity)
        )
    
    def attribute_drift(self, curr, ref):
        # SHAP 值分解：哪些输入特征导致输出分布变化
        shap_values = self.shap_explainer.shap_values(curr['feat_embed'])
        return rank_features_by_shap_drift(shap_values, ref['feat_embed'])

# Kubernetes/Service Mesh (Istio) 影子路由配置示例
apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: vsr-service
spec:
  hosts: ["vsr-inference"]
  http:
  - match:
    - headers:
        x-shadow-test: "true"  # 内部测试流量标记
    route:
    - destination:
        host: vsr-canary
        subset: v2.1.0-rc3
      weight: 100
  - route:
    - destination:
        host: vsr-stable
        subset: v2.0.5
      weight: 95
    - destination:
        host: vsr-canary
        subset: v2.1.0-rc3
      weight: 5  # 5% 真实流量镜像
    mirror:
      host: vsr-canary
      subset: v2.1.0-rc3
    mirrorPercentage:
      value: 100.0  # 100% 镜像流量到 Canary (不返回响应给用户)

graph LR
    Cloud[云端渲染 1080p/60fps] -->|H.265/HEVC 低延迟编码| Enc[编码器]
    Enc -->|RTP/UDP/SRT| Net[网络传输]
    Net --> Dec[客户端解码器]
    Dec -->|YUV 420 1080p| Pre[预处理: 去块效应/去色度锯齿]
    Pre --> EdgeSR[端侧轻量超分 2×→4K]
    EdgeSR -->|RGB 4K| Post[后处理: HDR Tone Mapping/锐化]
    Post --> Display[显示输出]
    
    Cloud -.->|Side Channel: Motion Vector/Depth/Normal| EdgeSR
    Cloud -.->|Side Channel: ROI Mask (Crosshair/UI)| EdgeSR

# 转码器码率控制回调伪代码
class VSR_Aware_RC:
    def __init__(self, nr_model, target_quality=0.85):
        self.nr_model = nr_model
        self.target_q = target_quality
        self.lambda_base = 1.0  # 基础 Lagrange 乘子
        
    def update_lambda(self, frame_idx, frame_type, complexity_feat):
        # 1. 预测当前帧超分后的质量潜力
        with torch.no_grad():
            pred_q = self.nr_model.predict_potential(complexity_feat) # 0~1
        
        # 2. 计算质量缺口
        gap = self.target_q - pred_q
        
        # 3. 动态调整 Lambda: 质量预期低 -> 分配更多比特 (降低 Lambda)
        # 使用 PID 控制器平滑调整
        self.lambda_cur = self.pid_controller.update(gap)
        
        # 4. 约束 Lambda 范围，防止码率失控
        return np.clip(self.lambda_cur, 0.3, 3.0)
    
    def on_frame_encoded(self, actual_bits, actual_q):
        # 闭环校准：实际编码质量 vs 预测质量
        self.pid_controller.feed_error(self.target_q - actual_q)