06b98d4b476bc78ca7f786e440b5fd163b60fab7
5
Commits
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 prosolis
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06b98d4b47 |
Price cycles in the mode decision: 37 misses become 1, for 0.26 dB
The decoder has been CPU-bound since FINDINGS 28 while the mode decision
minimised D + lam*R -- distortion against BYTES. decide() now minimises
D + lam*bytes + mu*cycles, and ratectl bisects mu per frame against the
833,333-cycle budget with the lam bisection nested inside it. On the worst
sustained window:
sasi 27.22 -> 26.95 dB, 109.5 -> 109.4 KB/s, 37/120 misses -> 1
scsi 29.90 -> 29.27 dB, 280.0 -> 278.6 KB/s, 51/120 misses -> 1
Bitrate does not move: the byte controller still binds, and mu changes WHICH
modes are bought. V4 is what it stops buying -- 25.2 -> 20.3% of blocks at sasi
and 15.0 -> 5.3% at scsi, where RAW takes it. That is 28.8's inversion in
practice: RAW is dearer in bytes and cheaper in cycles, so only the byte-rich
profile can buy its way out of V4.
Three things worth knowing beyond the headline:
- The one frame that still misses, at both profiles, is FRAME 0 -- no previous
reconstruction, so 100% changed by definition, which is also what a scene
cut is. It comes out at the all-V1 floor of 110.6% and is emitted late on
purpose. Freezing a cut to make a deadline is the worse failure.
- 28.7's "11 frames are impossible" was too pessimistic. That floor held the
SKIP set fixed and asked how cheaply the drawn blocks could be drawn; the
real decision can also MOVE a block to SKIP, which above ~90% non-SKIP is
the only lever left.
- SKIP's price depends on its neighbours (13.25 cycles clustered, 45 mixed),
which a per-block lagrangian cannot see. The way out is that the two uses
need not share a cost function: a ranking constant inside decide(), the
exact clustered rule for the frame-level bisection. vq_hybrid.cycles() is
now the one definition of that rule and 11_cpu_budget.py imports it.
Gated: 09_ratectl_drift.py runs both controllers, both 0/120 drifting frames.
The cost-aware container decodes pixel-exact on the 68000 (120 frames). ON by
default in encode.py; --no-cpu-fit restores session 7. check.sh ALL GREEN.
Still a model, not a measurement, for THIS container: FINDINGS 31's cycle
figures come from vq_hybrid.cycles (within 1 point of the 68000 on four frames
of the session-7 container). Timing this one on the machine is step 1 of the
next session -- it was started and killed for time, and it is slow.
FINDINGS 31. tools/analysis/13_cpu_ratectl.py.
Claude-Session: https://claude.ai/code/session_01194oWYW8DQXK1SZ2DnChW6
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prosolis
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497f88b945 |
Rate control: rebuilt per-frame, wired in, and gated at zero drift
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 |
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prosolis
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145753c0bf |
Handoff: rate control is next, and it is unsound as written
Session 5 handoff. The user has chosen rate control as the next session's work, so this reads ratectl.py properly before that session starts rather than discovering the problem mid-implementation. FINDINGS 26: encode_rate_controlled() is not sound. H.encode() is temporally recursive -- SKIP blocks copy the previous RECONSTRUCTION -- but rate control builds a ladder of independent whole-sequence encodes and picks each frame from whichever rung fits the budget. Frames then reference reconstructions the decoder never saw. Measured on the Singe window: 67 rung switches, 111 of 120 frames drift, worst frame 43.4% of pixels, reported PSNR overstated by 0.36 dB. It would have wired up cleanly and reported a plausible wrong answer. Two further defects in the same function: the lam ladder runs to 2e5, 250x past the FINDINGS 15 cliff, so a frame that only fits up there is destroyed rather than rate-controlled; and with 5 rungs only two are ever chosen, straddling the operating point by 7.5x. The docstring describes a per-frame binary search, which is the right design -- the implementation is a fixed ladder. The leaky bucket does work and should be kept: 109.1 KB/s against a 110 target. tools/analysis/09_ratectl_drift.py is the regression test and the acceptance criterion: it exits non-zero until zero frames drift. Also corrected the stale 38% blit figure in ratectl.py's profile commentary, which session 5 measured at 53.6% (FINDINGS 24), and recorded the pgrep -f self-kill trap again -- four times across three sessions now. check.sh ALL GREEN. Claude-Session: https://claude.ai/code/session_01194oWYW8DQXK1SZ2DnChW6 |
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prosolis
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f0f2f807a4 |
Raise both quality profiles; rule out entropy coding on CPU grounds
The profiles shipped in
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prosolis
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e4062ed294 |
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 |