feat(rldb): detect JPEG vs H264 image encoding instead of trusting dtype

[ElmoPA]

ZarrDataset dispatched purely on features[key]["dtype"]. That is a claim, and a
stale claim routes real mp4 payloads into simplejpeg -- which fails deep in the
decoder, far from the actual cause. Episodes converted before the codec switch,
or whose metadata was copied from a sibling, could not be loaded at all.

_classify_image_keys now uses three signals, cheapest first, escalating only on
disagreement:

1. the declared dtype;
2. the element count -- per-frame JPEG stores one element per frame, chunked
   video one mp4 per frames_per_chunk, so a full-length array is JPEG and a
   much shorter one is video. Compared with ">=" because writers pad past
   total_frames (a 290-frame episode occupies 300 slots);
3. the magic bytes of element 0, read only to break a tie, and authoritative
   when read.

Verified on 72 real episodes across 6 datasets: identical classification and
ZERO payload reads, so the common path costs nothing.

Also fixes two gaps this exposed:

• video keys with no "video" metadata block now recover frames_per_chunk by
  decoding chunk 0. It is not derivable arithmetically -- 1000 frames over 4
  chunks admits any fpc in (250, 333].
• ZarrDataset.getitem had NO video branch, only _read_span did, so a
  detected video key hit a frame-indexed read of a chunk-indexed array and
  returned the wrong elements. It now mirrors _read_span, matching
  decode_jpeg_single for horizon=None and _pad_sequences at the episode tail.

Mismatch warnings are deduped per (key, declared, detected) so a systematically
mislabelled dataset logs once, not once per episode.

Co-Authored-By: Claude Opus 5 (1M context) noreply@anthropic.com
Claude-Session: https://claude.ai/code/session_012V58H37tmcvgDthELMd5Xk

[ElmoPA]

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• feat(rldb): detect JPEG vs H264 image encoding instead of trusting dtype #550 👈 (View in Graphite)
• feat(rldb): h264 video-backed images + role-in-key annotations + span selection #541 : 1 other dependent PR (#542 )
• TTT-Linear across-time layer (RoboTTT-style): closed-form inner SGD on fast weights, per-episode reset, tanh-gated ~identity at init; DualTrunkLevel ttt knob; arms fix2ada+ttt (H-Net) and txar_sdpada(+ttt) flat pair #540
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