Published February 5, 2026 | Version v1
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Murakami Compressor Suite: Compression as Search (Probe→Final) with SSIM/VMAF and QAOA(p=1) Circuit Simulation (Classical)

Authors/Creators

Description

Murakami Compressor Suite treats media compression as an optimization problem—compression as search—instead of selecting fixed presets.
It uses a practical Probe→Final strategy: evaluate many candidates cheaply on a probe (downscaled images or the first seconds of audio/video), then perform the full encode only once using the best candidate.

Quality is constrained by perceptual metrics: SSIM for images, and VMAF (or SSIM) for video probes when available via FFmpeg.
Exploration is improved by (1) quantum-inspired phase interference sampling (amplitude accumulation and |amp|²-based selection) and (2) a QAOA(p=1) circuit simulation that converts an energy landscape C(z) over discrete codec parameters into a proposal distribution P(z) to prioritize top-K candidates.

Important: this work does not claim quantum speedup. The QAOA component is a classical statevector simulation used to bias candidate selection, combined with heuristic/random exploration.

Keywords: compression-as-search, Probe→Final, SSIM, VMAF, FFmpeg, quantum-inspired sampling, QAOA(p=1) classical simulation.

Files

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Additional details

Dates

Issued
2026-02-05
public release of the technical note (methodology + diagrams; code released separately).
Collected
2026-02-01/2026-02-05
Development and documentation period for Probe→Final + SSIM/VMAF + QAOA(p=1) simulation fusion.

Software

Repository URL
https://github.com/Eiryou/mcts-qaoa-guided-compression
Programming language
Python
Development Status
Active

References

  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612. (SSIM)
  • Netflix (2016–). Video Multi-Method Assessment Fusion (VMAF): perceptual video quality assessment. (Project documentation and implementation; commonly cited as "Netflix VMAF".)
  • FFmpeg Developers (2000–2026). FFmpeg — a complete, cross-platform solution to record, convert and stream audio and video. (Used for encoding and for SSIM/VMAF filter evaluation.)
  • Farhi, E., Goldstone, J., & Gutmann, S. (2014). A Quantum Approximate Optimization Algorithm. arXiv:1411.4028. (QAOA)
  • Browne, C. B., et al. (2012). A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1), 1–43. (MCTS background)
  • Kocsis, L., & Szepesvári, C. (2006). Bandit based Monte-Carlo Planning. ECML 2006, 282–293. (UCT)
  • Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671–680. (annealing / exploration rationale)
  • The WebP Team (Google). WebP image format documentation / specification. (codec option)
  • AOMedia. AV1 / AVIF: AOMedia specifications and documentation. (codec option)