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Robust pixel-wise illuminant estimation algorithm for images with a low bit-depth.

Shuwei Yue, Minchen Wei

|November 14, 2024

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This summary is machine-generated.

Pixel-wise illuminant estimation accuracy significantly drops with lower bit-depth images due to detail loss and noise. A new method combining L1 loss and physical post-processing boosts accuracy by 40% compared to deep learning models.

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Area of Science:

Computer Vision
Image Processing

Background:

Traditional illuminant estimation methods assume uniform illumination.
Pixel-wise methods offer broader scene applicability but face challenges with varying image bit-depths.
Lower bit-depth images, preferred by image signal processing (ISP) pipelines, degrade pixel-wise algorithm performance by up to 30% due to detail loss and increased noise.

Purpose of the Study:

To analyze the impact of bit-depth reduction on pixel-wise illuminant estimation.
To propose a novel method that overcomes the accuracy degradation in lower bit-depth images.
To improve illuminant estimation accuracy beyond current state-of-the-art deep neural network (DNN) methods.

Main Methods:

Investigated the causes of accuracy reduction in lower bit-depth images, identifying detail loss and noise as key factors.
Developed a new illuminant estimation approach integrating L1 loss optimization.
Incorporated physical-constrained post-processing to refine estimation results.

Main Results:

The proposed method demonstrated a significant improvement in estimation accuracy.
Achieved approximately 40% higher accuracy compared to existing state-of-the-art DNN-based methods.
Effectively mitigated the negative impact of lower bit-depths on illuminant estimation.

Conclusions:

Bit-depth reduction in image signal processing pipelines negatively impacts pixel-wise illuminant estimation accuracy.
The proposed L1 loss and physical post-processing method offers a robust solution for accurate illuminant estimation across different bit-depths.
This work advances the field of illuminant estimation, particularly for applications involving image signal processing pipelines.