Session 2: hybrid VQ codec, two quality profiles, three corrections

Answers session 1's critical-path question. Flat 4x4 VQ at k=256 was prototyped
and REJECTED by eye: Dirk's face disintegrates and ink outlines break into
4-pixel stair-steps. The 256-colour palettised frame is excellent, so the
palette was never the problem -- block VQ was.

Replaced it with a Cinepak-style hybrid: each 4x4 block is SKIP, one 4x4
codeword, four 2x2 codewords, or RAW literal pixels, chosen per block by
rate-distortion. The RAW escape makes lam=0 pixel-exact (measured 0.00 dB loss),
so the quality knob spans lossless to heavily-compressed in one bitstream.

Per the user's decision, ships TWO quality profiles from that one codec, one
decoder and one bitstream -- only the rate knob differs:
  sasi  45 KB/s  lam=300  34.8 dB   stock 10MHz ACE/EXPERT
  scsi  75 KB/s  lam=100  35.9 dB   Super/XVI or CZ-6BS1

Three corrections to earlier numbers:

1. Session 1's "183 KB/s at 12fps" was a bad extrapolation. Halving the
   framerate does not halve the bitrate -- decimation roughly doubles the
   per-frame delta. Re-measured directly: 340 KB/s for session 1's own RLE,
   247 KB/s for changed-spans+deflate. The lossless floor is 319 MB.

2. A FOURTH false-good result, same family as the three in FINDINGS 4:
   k=1024 codebooks appeared to buy +2.4 dB free, because the rate model
   charged 1 byte for a 10-bit index. Charging the true cost reverses the
   verdict -- k=256 wins at every matched bitrate, and by 5 dB at the low end
   where the SASI profile lives. k=256 ships.

3. Stream inventory: the ~3-5MB clips are 1.2-1.7s, not ~60s, and some 60s
   streams are menus, not content. Any survey must classify before averaging.

Also cleared both candidate sources for the game-logic layer: the SNES project
is MIT and DirkSimple is zlib, so the arcade scene graph can be imported and
the two transcriptions diffed against each other.

Encoder is working end-to-end: extract.py -> vq/vq_hybrid/ratectl -> encode.py,
emitting a big-endian DLX1 container the 68000 can parse with plain moves.

Claude-Session: https://claude.ai/code/session_01194oWYW8DQXK1SZ2DnChW6

tools/encoder/ratectl.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+"""Rate control: hit a target bitrate exactly, so one encoder serves both targets.
+
+USER DECISION (session 2): ship TWO quality modes, SASI and SCSI. The codec's
+bitrate ceiling is a build parameter; the encoder is otherwise identical.
+
+Mechanism: the hybrid encoder's lagrangian `lam` trades distortion for bytes
+monotonically, so per frame we binary-search lam to land inside a byte budget.
+A leaky bucket lets a quiet frame bank bytes that an action frame can spend --
+without that, quiet frames waste budget and action frames stay ugly.
+
+The ceiling is HARD: the 68000 streams at a fixed rate off the disk, and a frame
+that overruns is a dropped frame, not a slow frame.
+"""
+import numpy as np
+import vq_hybrid as H
+
+# Profiles. Bandwidths are the sustained-read figures the player can rely on;
+# see docs/FINDINGS.md 5 -- these are FOLKLORE-grade until the disk benchmark
+# is unblocked, so they are deliberately conservative fractions of the quoted
+# ceiling (audio, seeks and container overhead come out of the same pipe).
+# Calibrated against the CORRECTED rate-distortion measurement (FINDINGS 14).
+#
+# k=256 with 1-byte indices beats k=1024 with 2-byte indices at every matched
+# bitrate. The earlier "+2.4 dB for k=1024" was an artifact of a rate model that
+# charged 1 byte for a 10-bit index. 1-byte indices also mean the 68000 decoder
+# reads a plain move.b with no alignment case, and the codebook is 8 KB not 32 KB.
+#
+# The two profiles are the SAME codec, decoder and bitstream -- only `lam` differs.
+PROFILES = {
+    "sasi": dict(kbps=45,  lam=300.0, k1=256, k4=256,
+                 desc="stock 10MHz ACE/EXPERT, SASI",
+                 quality="34.8 dB on 00020 / 28.3 dB on 00146"),
+    "scsi": dict(kbps=75,  lam=100.0, k1=256, k4=256,
+                 desc="Super/XVI, or CZ-6BS1 board in a 10MHz machine",
+                 quality="35.9 dB on 00020 / 29.0 dB on 00146"),
+}
+# Not a shipping profile, but the curve continues: lam=25 is ~185 KB/s at ~38.7 dB
+# with 26% RAW blocks, and lam->0 is pixel-exact (0.00 dB loss). Entropy-coding
+# the payload (NOT YET IMPLEMENTED) should shift the whole curve ~1.4x left.
+
+AUDIO_KBPS = 7.8          # MSM6258 ADPCM 15.6kHz mono -- comes out of the same budget
+
+
+def frame_budget(kbps, fps=12, audio=AUDIO_KBPS):
+    """bytes per video frame after audio takes its cut"""
+    return (kbps - audio) * 1024.0 / fps
+
+
+def encode_rate_controlled(m, target_kbps, fps=12, bucket_frames=8,
+                           lam_lo=1.0, lam_hi=2e5, steps=9, verbose=False):
+    budget = frame_budget(target_kbps, fps)
+    bucket = 0.0                       # banked bytes, capped at bucket_frames*budget
+    cap = bucket_frames * budget
+    out_recon, out_modes, out_sizes, out_lam = [], [], [], []
+
+    # encode() is whole-sequence; drive it per-lam and pick per frame.
+    # Cheaper than re-running the whole encoder per frame: precompute the ladder.
+    ladder = []
+    lams = np.geomspace(lam_lo, lam_hi, steps)
+    for lam in lams:
+        e = H.encode(m, lam=float(lam))
+        ladder.append(e)
+        if verbose:
+            print(f"  lam={lam:9.0f}  mean {e['sizes'].mean():6.0f} B/frame")
+
+    nf = len(m["idx"])
+    for f in range(nf):
+        allow = budget + bucket
+        # cheapest lam (highest quality) whose size fits the allowance
+        pick = len(lams) - 1
+        for i in range(len(lams)):
+            if ladder[i]["sizes"][f] <= allow:
+                pick = i; break
+        sz = ladder[pick]["sizes"][f]
+        bucket = min(cap, bucket + budget - sz)
+        out_recon.append(ladder[pick]["recon"][f])
+        out_modes.append(ladder[pick]["modes"][f])
+        out_sizes.append(sz); out_lam.append(lams[pick])
+
+    return dict(recon=out_recon, modes=out_modes, sizes=np.array(out_sizes),
+                lam=np.array(out_lam), nb=ladder[0]["nb"], budget=budget)
+
+
+def summarise(m, enc, target_kbps, fps=12):
+    import vq as VQ
+    pal = m["pal"]
+    rec = [pal[i] for i in enc["recon"]]
+    src = [pal[i] for i in m["idx"]]
+    p = np.mean([VQ.psnr(o, v) for o, v in zip(m["rgb"], rec)])
+    pp = np.mean([VQ.psnr(o, v) for o, v in zip(m["rgb"], src)])
+    sz = enc["sizes"]
+    mo = np.concatenate(enc["modes"])
+    return dict(target=target_kbps, psnr=p, pal=pp, loss=pp - p,
+                mean_B=sz.mean(), max_B=sz.max(), budget=enc["budget"],
+                kbps=sz.mean() * fps / 1024 + AUDIO_KBPS,
+                over=100.0 * np.mean(sz > enc["budget"]),
+                skip=100 * (mo == 0).mean(), v1=100 * (mo == 1).mean(),
+                v4=100 * (mo == 2).mean())