photo_judgers/docs/research-brisque-niqe.md

BRISQUE / NIQE — Classical No-Reference IQA

Model Info

• BRISQUE: "No-reference Image Quality Assessment in the Spatial Domain" — Mittal et al., IEEE TIP 2012
• NIQE: "Making a 'Completely Blind' Image Quality Analyzer" — Mittal et al., IEEE SPL 2013
• Python packages:
  • pip install brisque[opencv-python] — Latest BRISQUE (supports opencv-contrib, opencv-headless)
  • pip install pybrisque — Alternative BRISQUE (older, from Bukalapak)
  • pip install image-quality — Contains imquality.brisque module
  • pip install pyiqa — IQA-PyTorch toolbox (includes BRISQUE + NIQE + 30+ other metrics)
  • pip install nr-iqa — Comprehensive library (BRISQUE + NIQE + PIQE)
• License: Academic (BRISQUE: BSD-like via LearnOpenCV; NIQE: academic use)
• Dependencies: OpenCV, NumPy, SciPy (BRISQUE); skvideo + NumPy (NIQE)

Architecture

BRISQUE (Blind/Referenceless Image Spatial Quality Evaluator)

• Type: Handcrafted features + Support Vector Regression (SVR)
• Pipeline:
  1. MSCN (Mean Subtracted Contrast Normalized) coefficients — decorrelates image from local brightness/contrast
  2. GGD (Generalized Gaussian Distribution) fitting to MSCN coefficients — extracts shape parameter alpha (tail heaviness)
  3. Pairwise products in 4 orientations (horizontal, vertical, 2 diagonals) — 16 features
  4. AGGD (Asymmetric GGD) fitting to pairwise products
  5. Multi-scale analysis at 2 scales (original + 0.5x downsampled) — 36-D feature vector total (18 per scale)
  6. SVR prediction maps 36-D features to quality score in [0, 100]
• Training data: TID2008 dataset (human-rated distorted images, MOS scores 0-100)
• Score direction: Lower = better (0 = perfect, 100 = worst quality)

NIQE (Natural Image Quality Evaluator)

• Type: Handcrafted features + Multivariate Gaussian (MVG) model
• Key difference from BRISQUE: Completely opinion-unaware — does NOT need human-rated distorted images for training
• Pipeline:
  1. Extract 18-D NSS features from 96x96 patches (same as BRISQUE scale-1)
  2. Fit MVG model (mu_ref, Sigma_ref) to pristine images — the "naturalness" reference
  3. Fit MVG (mu_test, Sigma_test) to test image patches
  4. Quality = Mahalanobis distance between the two MVG models
• Training data: Only pristine (undistorted) images — no human ratings needed
• Score direction: Lower = better (0 = perfectly natural, higher = more distorted)

Installation

# Option 1: Latest BRISQUE (recommended)
pip install brisque[opencv-python]

# Option 2: For servers/Docker (headless)
pip install brisque[opencv-python-headless]

# Option 3: Comprehensive NR-IQA library (BRISQUE + NIQE + PIQE)
pip install nr-iqa

# Option 4: IQA-PyTorch toolbox (BRISQUE + NIQE + 30+ other metrics)
pip install pyiqa

Python Usage

# --- BRISQUE via brisque package ---
from brisque import BRISQUE
import cv2

brisque = BRISQUE()
img = cv2.imread('photo.jpg')
score = brisque.score(img)  # Returns float in [0, 100]
print(f"BRISQUE: {score:.2f}")  # Lower = better

# --- BRISQUE via image-quality package ---
from imquality.brisque import BRISQUE
from PIL import Image

img = Image.open('photo.jpg')
score = BRISQUE().score(img)  # Returns float in [0, 100]

# --- NIQE via nr-iqa package ---
from nr_iqa import NIQE
import cv2

niqe = NIQE()
img = cv2.imread('photo.jpg', cv2.IMREAD_GRAYSCALE)
score = niqe.score(img)  # Returns float in [0, 100]
print(f"NIQE: {score:.4f}")  # Lower = better

# --- Both via IQA-PyTorch toolbox ---
import pyiqa
device = 'cuda' if torch.cuda.is_available() else 'cpu'

brisque = pyiqa.create_metric('brisque', device=device)
niqe = pyiqa.create_metric('niqe', device=device)

score_b = brisque('./photo.jpg')
score_n = niqe('./photo.jpg')

print(f"BRISQUE: {score_b:.2f}, NIQE: {score_n:.4f}")

# Batch processing
import glob
for path in glob.glob('./wedding-photos/*.jpg'):
    score = brisque(path)

Score Interpretation for Wedding Photos

BRISQUE Score	NIQE Score	Interpretation	Recommended Action
0-10	0-3	Excellent technical quality	Auto-keep
10-25	3-6	Good technical quality	Keep
25-40	6-10	Acceptable, minor issues	Review
40-60	10-15	Poor technical quality	Likely reject
60-100	15+	Very poor (blurry, noisy, compressed)	Auto-reject

Recommended thresholds for wedding culling:

• BRISQUE < 30 AND NIQE < 8 -> keep (good technical quality)
• BRISQUE > 60 OR NIQE > 15 -> reject (poor technical quality)
• In between -> manual review

Performance Benchmarks

Metric	SRCC vs Human	PLCC vs Human	Notes
BRISQUE (TID2008)	~0.85	~0.87	Trained on TID2008
NIQE (KADID-10k)	~0.83	~0.85	Opinion-unaware, generalizes well
BRISQUE (live)	~0.81	~0.83	Live (no-reference) dataset
NIQE (live)	~0.80	~0.82	Live (no-reference) dataset

Hardware Requirements

• CPU-only: No GPU needed — purely classical (no neural networks)
• Speed per image: ~50-200ms per image on modern CPU (single-core)
• Speed for 4,000 images: ~3-15 minutes on CPU (single-core, depends on image size)
• Multi-threading: Can parallelize across CPU cores — ~1-3 minutes with 8+ cores
• VRAM: None required (BRISQUE/NIQE are pure NumPy/OpenCV)
• Memory usage: ~50-100 MB RAM for batch processing

What It's Good At

• No GPU needed: Runs on any CPU, even low-end machines
• Fast: Good for quick technical quality screening — faster than any deep learning model
• Detects blur and compression artifacts: Excellent at detecting out-of-focus shots, motion blur, JPEG artifacts, noise
• Simple: Minimal dependencies (OpenCV + NumPy + SciPy)
• Deterministic: No randomness — same image always gets same score
• Lightweight: ~50 MB total install size, no model weights to download
• Good complement to aesthetic models: Catches technical rejects that aesthetic models miss
• Works on any image type: Not biased toward any particular visual style

What It's Bad At

• Purely technical: Scores only technical quality (blur, noise, compression), not aesthetics at all
• Poor on artistic/creative images: Penalizes artistic choices — shallow depth of field, high contrast, film grain, vignette, intentional blur
• Outdated methodology: 2012-2013 techniques; inferior to modern deep learning approaches for complex distortions
• Sensitive to intentional artistic effects: May score a portrait with beautiful bokeh (shallow DOF) as "low quality"
• Color-blind: BRISQUE operates on luminance channel; NIQE on grayscale — ignores color information entirely
• Not suitable as sole scorer: Must be combined with an aesthetic model for wedding photo sorting
• No multi-scale like MUSIQ: Only processes at one scale (BRISQUE does 2-scale but simpler than MUSIQ's 3-scale)
• Training data limitations: BRISQUE trained on TID2008 (lab-distorted images); may not generalize well to real-world wedding photo distortions