FINDINGS 26 stopped the session-5 rate controller before it shipped: it built a
lam-ladder of independent whole-sequence encodes and picked frames off it, so
SKIP blocks referenced reconstructions the decoder never saw -- 111 of 120
frames drifted. The fix is the structural one 26.1 said it had to be.
vq_hybrid is now frame-drivable -- frame_ctx / decide / paint -- and encode() is
a thin loop over it. Rate control drives the same three calls, bisects lam per
frame under the leaky bucket, and feeds back the frame it actually emitted. The
desync has no way to occur, and 09_ratectl_drift.py goes 111/120 -> 0/120. That
test is now part of check.sh, which is ~2 min rather than ~40 s.
Both overshoots on the worst sustained window are closed for under 1 dB, totals
including audio: sasi 137.4 -> 109.5 KB/s (-0.60 dB), scsi 381.6 -> 280.0 KB/s
(-0.91 dB). Zero frames hit the lam=800 cliff, so nothing was destroyed to get
there. Rate control also makes the display path cheaper -- scsi's median drops
53.6% -> 47.1% -- because raising lam moves blocks to SKIP and V1.
Two knobs measured rather than guessed. --rc-floor is worth 0.00 dB on that
window and defaults to the profile lam, so rate control cannot regress content
that already fits. --prefill defaults to 0 and is documented as a trap: it buys
a permission to overshoot of exactly bucket/nframes, and on a 14-frame clip it
disables the controller outright.
FINDINGS 26.5 was wrong in both halves and 27.6 records it. _paint was not the
bottleneck (14% of a frame, though vectorising it was still right at 17.1x) and
the ladder was never "minutes" -- those were k-means in build(). What makes
per-frame rate control affordable is that VQ.assign depends on neither lam nor
prev, so it is cached one frame deep: a 12-step search over 120 frames costs
0.31 s against 49.1 s.
Also caught: fixed-lam sasi was already 5% over target on 00020, the clip
everyone called easy. Nothing noticed because the profile table quotes PSNR and
not bitrate.
check.sh: ALL GREEN.
Claude-Session: https://claude.ai/code/session_01194oWYW8DQXK1SZ2DnChW6
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
prosolis