diff --git a/.memory/worklog.json b/.memory/worklog.json
index 6857c51..f3c50d0 100644
--- a/.memory/worklog.json
+++ b/.memory/worklog.json
@@ -3160,6 +3160,19 @@
       "type": "session-heartbeat",
       "message": "Codex 会话活跃 · 最近命令：codex · 3 项未提交变更 · 最近提交：auto-save 2026-05-14 04:04 (~6)",
       "files_changed": 3
+    },
+    {
+      "ts": "2026-05-14T04:10:26+08:00",
+      "type": "commit",
+      "message": "auto-save 2026-05-14 04:10 (~4)",
+      "hash": "0448d28",
+      "files_changed": 4
+    },
+    {
+      "ts": "2026-05-13T20:13:12Z",
+      "type": "session-heartbeat",
+      "message": "Claude 会话活跃 · 最近命令：claude · 3 项未提交变更 · 最近提交：auto-save 2026-05-14 04:10 (~4)",
+      "files_changed": 3
     }
   ]
 }
diff --git a/api/main.py b/api/main.py
index 1d81476..3173cfa 100644
--- a/api/main.py
+++ b/api/main.py
@@ -90,6 +90,7 @@ JobStatus = Literal[
 KEYFRAME_COUNT = int(os.getenv("KEYFRAME_COUNT", "5"))
 FrameExtractTarget = Literal["balanced", "subject", "transition", "expression", "motion"]
 FrameExtractMode = Literal["replace", "append"]
+FrameExtractQuality = Literal["fast", "accurate", "ultra"]
 FRAME_TARGET_LABELS: dict[FrameExtractTarget, str] = {
     "balanced": "综合关键帧",
     "subject": "清晰主体",
@@ -97,6 +98,11 @@ FRAME_TARGET_LABELS: dict[FrameExtractTarget, str] = {
     "expression": "表情瞬间",
     "motion": "动作峰值",
 }
+FRAME_QUALITY_LABELS: dict[FrameExtractQuality, str] = {
+    "fast": "快速",
+    "accurate": "精细",
+    "ultra": "极准",
+}
 
 
 class GeneratedImage(BaseModel):
@@ -399,20 +405,23 @@ def _sharpness_from_gray(g: np.ndarray) -> float:
     return float(lap.var())
 
 
-def _frame_metrics(img_path: Path, idx: int, timestamp: float) -> dict | None:
+def _frame_metrics(img_path: Path, idx: int, timestamp: float, metric_width: int = 160) -> dict | None:
     """低清候选帧的本地评分特征。只用于排序，最终仍从原视频抽原尺寸帧。"""
     try:
         with Image.open(img_path) as raw:
             img = raw.convert("RGB")
             h = imagehash.phash(img)
-            small = img.resize((160, 90))
+            src_w, src_h = img.size
+            metric_height = max(1, round(metric_width * src_h / max(src_w, 1)))
+            small = img.resize((metric_width, metric_height))
     except Exception:
         return None
 
     arr = np.asarray(small, dtype=np.float32)
     # Rec. 601 luma，保留 0-255 范围，便于和清晰度 / 对比度阈值一起看。
     gray = (0.299 * arr[:, :, 0] + 0.587 * arr[:, :, 1] + 0.114 * arr[:, :, 2]).astype(np.float32)
-    center = gray[22:68, 40:120]
+    gh, gw = gray.shape
+    center = gray[gh // 4:max(gh // 4 + 1, gh * 3 // 4), gw // 4:max(gw // 4 + 1, gw * 3 // 4)]
     rg = arr[:, :, 0] - arr[:, :, 1]
     yb = 0.5 * (arr[:, :, 0] + arr[:, :, 1]) - arr[:, :, 2]
     colorfulness = float(np.sqrt(rg.var() + yb.var()) + 0.3 * np.sqrt(rg.mean() ** 2 + yb.mean() ** 2))
@@ -432,6 +441,20 @@ def _frame_metrics(img_path: Path, idx: int, timestamp: float) -> dict | None:
     }
 
 
+def _scan_profile(duration: float, quality: FrameExtractQuality) -> tuple[float, int, int, int]:
+    """返回 scan_fps / scan_width / metric_width / estimated_count。"""
+    if quality == "ultra":
+        base_fps, scan_width, cap, metric_width = 12.0, 960, 1800, 320
+    elif quality == "accurate":
+        base_fps, scan_width, cap, metric_width = 8.0, 720, 900, 240
+    else:
+        base_fps, scan_width, cap, metric_width = 2.0, 360, 240, 160
+
+    estimated = max(1, min(int(duration * base_fps), cap))
+    scan_fps = max(0.02, min(base_fps, estimated / max(duration, 0.1)))
+    return scan_fps, scan_width, metric_width, estimated
+
+
 def _attach_temporal_metrics(items: list[dict]) -> None:
     """相邻低清帧差异：转场 / 动作目标依赖它，不需要逐帧高分辨率扫描。"""
     for i, it in enumerate(items):
diff --git a/docs/source-analysis.html b/docs/source-analysis.html
index 9e47a2e..c9e0469 100644
--- a/docs/source-analysis.html
+++ b/docs/source-analysis.html
@@ -723,7 +723,7 @@ api/main.py
           <tbody>
             <tr>
               <td><span class="tag blue">输入 Input</span></td>
-              <td>创建/上传任务，显示视频就绪；视频缩略图上方提供自动抽帧快捷工具条，可快速选目标、张数并多次追加；单击视频缩略图打开画布内抽帧面板。</td>
+              <td>创建/上传任务，显示视频就绪；每个视频缩略图上方都有绑定自己的自动抽帧快捷工具条，可快速选目标、张数并多次追加；单击视频缩略图打开画布内抽帧面板。</td>
               <td>不要自动一路跑到 ASR 或生图；用户需要控制解析节奏。</td>
               <td><code>page.tsx</code>、<code>InputNode</code>、<code>VideoFramePanelNode</code>、<code>api/main.py</code></td>
             </tr>
@@ -817,6 +817,18 @@ api/main.py
         <h2>变更记录</h2>
         <p>这个记录不是 git log 的替代品。它记录“产品理解发生了什么变化、影响了哪些源码、你以后描述需求时该怎么说”。后续每次改功能都要补一条。</p>
         <div class="changelog">
+          <article class="change">
+            <header>
+              <h3>2026-05-14 · 每个输入视频缩略图绑定自己的抽帧工具条</h3>
+              <span class="tag violet">Input</span>
+              <span class="tag blue">UX</span>
+            </header>
+            <div class="body">
+              <p><strong>问题：</strong>统一放在缩略图浮条上方的抽帧工具条仍然不够明确，用户无法一眼判断当前会抽哪一个视频。</p>
+              <p><strong>改动：</strong>抽帧目标、张数和抽帧按钮改为渲染在每个视频缩略图正上方，并且每个 job 独立保存目标和张数设置。点击某个缩略图上方的抽帧按钮时，前端直接把该 <code>jobId</code> 传给 <code>analyzeJob</code>，同时切换 active job 并进入该 job 的进度轮询。</p>
+              <p><strong>影响：</strong><code>web/app/page.tsx</code>、<code>web/components/nodes/index.tsx</code>、<code>docs/source-analysis.html</code>。后续与输入视频相关的快捷操作都应优先贴近对应缩略图，不再依赖全局当前选择的心智。</p>
+            </div>
+          </article>
           <article class="change">
             <header>
               <h3>2026-05-14 · 自动抽帧快捷工具条移到缩略图上方</h3>
diff --git a/web/components/nodes/index.tsx b/web/components/nodes/index.tsx
index 0def9a0..e3c9d54 100644
--- a/web/components/nodes/index.tsx
+++ b/web/components/nodes/index.tsx
@@ -507,7 +507,7 @@ export function InputNode({ data, selected }: NodeProps<{ data: NodeData }> | an
 
   return (
     <div ref={rootRef} className="relative" style={{ width: "100%", height: "100%" }}>
-      {/* 多视频缩略图浮条 — 「+」在最左，job 按时间倒序（最新靠左高亮），统一高度 64，宽度按视频原比例，一行横滚。
+      {/* 多视频缩略图浮条 — 「+」在最左，job 按时间倒序（最新靠左高亮），每个视频上方绑定独立抽帧快捷条。
           浮条宽度 = 节点宽度（节点拖宽后浮条同步变宽，可见更多缩略图，少滚动）。 */}
       {d.jobs.length > 0 && (
         <FloatingThumbnailStrip label="输入视频缩略图横向滑动条">