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HDR Video & VQ Metrics

A Slidev presentation covering HDR color science fundamentals and video quality metrics.

Authors: Emil Santurio · Manuel Barrabino


Running the presentation

npm install
npm run dev      # dev server with hot-reload at http://localhost:3030
npm run build    # static build
npm run export   # export to PDF

Structure

The deck is split across two complete parts.

Part I — Foundations

Six sections covering the technical substrate of HDR:

# Section Key topics
1 Color Vocabulary Gamut vs. model vs. space; CIE 1931 chromaticity diagram; BT.709 / DCI-P3 / BT.2020 primaries
2 Signal Plumbing YCbCr matrix coefficients (BT.709 vs. BT.2020); bit depth and the Barten model; narrow vs. full range; HEVC VUI signaling (colour_primaries, transfer_characteristics, matrix_coefficients)
3 The Transfer Function OETF vs. EOTF; why linear encoding wastes bits; SDR ceiling at ~100 nits
4 Why HDR Exists Luminance range (SDR ~10 stops vs. real world ~20 stops); wider gamut; perceptual precision
5 How HDR Solves It PQ (ST 2084) — absolute, display-referred, up to 10,000 nits; HLG — scene-referred, broadcast-compatible; static metadata (ST 2086 + CTA-861.3 MaxCLL/MaxFALL); dynamic metadata (HDR10+ / Dolby Vision); HDR format landscape
6 Recap & Handoff Consolidated summary; hand-off to Part 2

Part II — HDR Video Quality Metrics

Four sections on HDR-aware perceptual quality metrics:

# Section Key topics
1 Limitations of Traditional Metrics VMAF's SDR-centric design; EOTF and color space mismatch (PQ/BT.2020 vs. Gamma/BT.709)
2 HDRMAX Non-linear tone-mapping patch on top of VMAF; tradeoffs
3 ColorVideoVDP Perceptual model; inputs/outputs; JOD scoring; SRCC results vs. LIVE-HDR dataset
4 Hands-on Practical demo and metrics comparison

Live Demos (Part I)

Three runnable scripts in demos/part1-foundations/ designed to be executed alongside the slides. Full prerequisites and running instructions in the demo README.

# Script Ties to slide What it shows
1 01_ffprobe_hdr_inspection.sh HEVC VUI signaling Reads colour_primaries, transfer_characteristics, matrix_coefficients + ST 2086 / MaxCLL / MaxFALL from a real HDR10 file
2 02_wrong_matrix_red_shift.py YCbCr matrix coefficients Reproduces the slide's numerical example; generates a side-by-side PNG showing the color shift from using the wrong matrix
3 03_bit_depth_banding.sh Bit depth / Barten model Produces a static 8-bit vs 10-bit gradient comparison + fully signaled SDR and HDR10 playback files

Suggested order during the talk: Demo 1 after the VUI signaling slide → Demo 2 after the wrong-matrix numerical example → Demo 3 after the bit-depth slide.


Live Demos (Part II)

Workshop-style hands-on session in demos/part2-quality-metrics/, designed to be run alongside the Hands-on section of Part II. Full installation instructions and running order in the demo README.

The workshop uses ColorVideoVDP (included as a Git submodule) and a set of curated video assets. Install first with the platform-specific script, then activate the virtual environment before the talk.

# Exercise Ties to slide What it shows
0 Installation (install/Mac/setup.sh · install/Windows/setup.bat) Prerequisites Creates a venv, installs PyTorch + ColorVideoVDP, runs a sanity check
1 JOD comparison — blur vs. flicker ColorVideoVDP outputs Runs cvvdp on test-blur-20.mp4 and test-flicker-20.mp4 against ref.mp4; participants fill in the JOD table
2 JOD → human preference (jod-to-human-preference.py) Psychometric curve (ΔP formula) Converts raw JOD scores to percentage of observers who would prefer the original
3 Heatmap + distogram generation A-sust / A-trans / RG / YV channels Produces visual artifacts maps; participants diagnose why flicker is worse than blur
4 Bonus — display swap (standard_fhdstandard_4k) Display model & ppd Shows how blur JOD degrades on 4K while flicker JOD stays flat, validating the spatial vs. temporal channel split

Suggested order during the talk: Exercise 0 must run before the session → Exercise 1 after the HDRMAX limitations slide → Exercises 2–3 after the ColorVideoVDP inputs/outputs slide → Exercise 4 as a wrap-up bonus.

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