prosolis
f0f2f807a4
The profiles shipped in e4062ed were set far too low. 45 KB/s (sasi) and
75 KB/s (scsi) are 12% and 7% of the respective folklore bus figures. They had
been read off the knee of the rate-distortion curve and then presented as
though bandwidth-derived, which they were not.
Raised to sasi 110 KB/s (lam=60) and scsi 280 KB/s (lam=10) -- 35% and 28%
utilisation. scsi is now within 0.52 dB of the palette ceiling on scene 00020.
Checking the CPU side, which nobody had done for the decode path, produces a
second and more important result. Against the 833k cycle/frame budget at 12fps:
full-frame blit, every frame 319k 38% affordable
LZ4/LZSS decode ~30KB/frame 450k 54%
deflate decode ~30KB/frame 1800k 216% infeasible
So raising the VQ bitrate is nearly free -- RAW, the mode that dominates at
high rate, is the cheapest mode to blit -- but entropy coding is not viable at
all. That demotes the "247 KB/s lossless changed-spans+deflate" figure from
FINDINGS 8 to a compression upper bound rather than a shippable design, and
removes entropy coding from the roadmap. VQ is the right architecture precisely
because its decode is a table copy.
Also confirms the architecture unifies: the hybrid at lam=0 lands within 3% of
the purpose-built lossless coder, so there is no separate lossless path.
Consequence for planning: the blocked disk benchmark is now critical-path, not
optional. If SCSI sustains >=800 KB/s the correct scsi profile is lam=0 --
pixel-exact video at ~450 KB/s and 38% CPU. Whether this port ships transparent
or lossy on SCSI is waiting on one measurement.
Claude-Session: https://claude.ai/code/session_01194oWYW8DQXK1SZ2DnChW6
113 lines
5.3 KiB
Python
113 lines
5.3 KiB
Python
#!/usr/bin/env python3
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"""Rate control: hit a target bitrate exactly, so one encoder serves both targets.
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USER DECISION (session 2): ship TWO quality modes, SASI and SCSI. The codec's
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bitrate ceiling is a build parameter; the encoder is otherwise identical.
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Mechanism: the hybrid encoder's lagrangian `lam` trades distortion for bytes
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monotonically, so per frame we binary-search lam to land inside a byte budget.
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A leaky bucket lets a quiet frame bank bytes that an action frame can spend --
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without that, quiet frames waste budget and action frames stay ugly.
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The ceiling is HARD: the 68000 streams at a fixed rate off the disk, and a frame
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that overruns is a dropped frame, not a slow frame.
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"""
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import numpy as np
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import vq_hybrid as H
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# Profiles. Bandwidths are the sustained-read figures the player can rely on;
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# see docs/FINDINGS.md 5 -- these are FOLKLORE-grade until the disk benchmark
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# is unblocked, so they are deliberately conservative fractions of the quoted
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# ceiling (audio, seeks and container overhead come out of the same pipe).
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# Calibrated against the corrected rate-distortion curve (FINDINGS 14-15) AND
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# against the bus and CPU budgets (FINDINGS 17). Session 2 initially set these
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# far too low: 45 / 75 KB/s is 12% of the pessimistic SASI folklore figure and
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# 7% of the SCSI one. Nothing justified that -- the numbers were read off the
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# knee of the RD curve, not off the hardware.
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#
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# What actually bounds the high end:
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# - Bus: unmeasured. ~300-500 KB/s SASI / ~1 MB/s SCSI, both FOLKLORE.
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# This is the binding unknown and the reason the disk benchmark matters.
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# - CPU: a FULL-frame blit is only 38% of the 12fps budget, and VQ decode is
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# table copies (RAW, the mode that dominates at high rate, is the CHEAPEST
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# to blit). So raising the bitrate is nearly free on CPU.
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# - Entropy coding is NOT the way to buy headroom here: deflate decode is
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# ~216% of the frame budget on a 68000 and even LZ4 is ~54%. See FINDINGS 17.
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# The rates below are therefore RAW payload, no entropy coding.
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#
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# The two profiles are the SAME codec, decoder and bitstream -- only `lam` differs.
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PROFILES = {
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"sasi": dict(kbps=110, lam=60.0, k1=256, k4=256,
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desc="stock 10MHz ACE/EXPERT, SASI",
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quality="36.9 dB on 00020 / 29.6 dB on 00146",
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util="~105 KB/s = 35% of the pessimistic 300 KB/s SASI figure"),
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"scsi": dict(kbps=280, lam=10.0, k1=256, k4=256,
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desc="Super/XVI, or CZ-6BS1 board in a 10MHz machine",
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quality="39.4 dB on 00020 / 32.3 dB on 00146",
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util="~275 KB/s = 28% of the 1 MB/s SCSI folklore figure"),
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}
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# lam=0 is PIXEL-EXACT against the palettised frame (0.00 dB loss) at ~450 KB/s
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# of raw payload, and costs only 38% of the CPU budget. If the blocked disk
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# benchmark confirms SCSI sustains >=800 KB/s, the `scsi` profile should become
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# lam=0 and the port ships transparent video. That decision is waiting on a
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# measurement, not on a design choice.
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AUDIO_KBPS = 7.8 # MSM6258 ADPCM 15.6kHz mono -- comes out of the same budget
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def frame_budget(kbps, fps=12, audio=AUDIO_KBPS):
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"""bytes per video frame after audio takes its cut"""
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return (kbps - audio) * 1024.0 / fps
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def encode_rate_controlled(m, target_kbps, fps=12, bucket_frames=8,
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lam_lo=1.0, lam_hi=2e5, steps=9, verbose=False):
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budget = frame_budget(target_kbps, fps)
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bucket = 0.0 # banked bytes, capped at bucket_frames*budget
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cap = bucket_frames * budget
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out_recon, out_modes, out_sizes, out_lam = [], [], [], []
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# encode() is whole-sequence; drive it per-lam and pick per frame.
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# Cheaper than re-running the whole encoder per frame: precompute the ladder.
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ladder = []
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lams = np.geomspace(lam_lo, lam_hi, steps)
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for lam in lams:
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e = H.encode(m, lam=float(lam))
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ladder.append(e)
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if verbose:
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print(f" lam={lam:9.0f} mean {e['sizes'].mean():6.0f} B/frame")
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nf = len(m["idx"])
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for f in range(nf):
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allow = budget + bucket
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# cheapest lam (highest quality) whose size fits the allowance
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pick = len(lams) - 1
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for i in range(len(lams)):
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if ladder[i]["sizes"][f] <= allow:
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pick = i; break
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sz = ladder[pick]["sizes"][f]
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bucket = min(cap, bucket + budget - sz)
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out_recon.append(ladder[pick]["recon"][f])
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out_modes.append(ladder[pick]["modes"][f])
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out_sizes.append(sz); out_lam.append(lams[pick])
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return dict(recon=out_recon, modes=out_modes, sizes=np.array(out_sizes),
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lam=np.array(out_lam), nb=ladder[0]["nb"], budget=budget)
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def summarise(m, enc, target_kbps, fps=12):
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import vq as VQ
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pal = m["pal"]
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rec = [pal[i] for i in enc["recon"]]
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src = [pal[i] for i in m["idx"]]
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p = np.mean([VQ.psnr(o, v) for o, v in zip(m["rgb"], rec)])
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pp = np.mean([VQ.psnr(o, v) for o, v in zip(m["rgb"], src)])
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sz = enc["sizes"]
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mo = np.concatenate(enc["modes"])
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return dict(target=target_kbps, psnr=p, pal=pp, loss=pp - p,
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mean_B=sz.mean(), max_B=sz.max(), budget=enc["budget"],
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kbps=sz.mean() * fps / 1024 + AUDIO_KBPS,
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over=100.0 * np.mean(sz > enc["budget"]),
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skip=100 * (mo == 0).mean(), v1=100 * (mo == 1).mean(),
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v4=100 * (mo == 2).mean())
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