Content-Adaptive Perceptual Quantizer for HDR Image Encoding

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Solution Overview

Problem

Current technologies face challenges in efficiently encoding and displaying high dynamic range (HDR) images due to limitations in bit depth and compression standards, leading to suboptimal representation and display of HDR content on conventional displays.

Innovation Solution

The implementation of content-adaptive perceptual quantization techniques, which involve noise-mask generation, histogram calculation, and codeword mapping to dynamically adjust bit depth based on image content, allowing for efficient encoding and display of HDR images within existing infrastructure.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If high-precision floating-point formats (e.g., 16-bit) are used to store and distribute HDR images, then the dynamic range and visual fidelity are improved, but the data size and processing complexity increase significantly

Engineering Contradiction:

Improvebit depth precisionVSAvoiddata size

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent applies perceptual quantization to transform HDR image data from high-precision floating-point format to lower-precision fixed-point format by changing the numerical representation parameters. This transformation reduces data size while maintaining visual fidelity by allocating bits according to perceptual importance rather than uniform precision

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements content-adaptive quantization that applies different bit depths to different regions of the image based on local content characteristics. Smooth regions receive higher precision while textured regions receive lower precision, optimizing the balance between data size and visual quality

Inventive Principle:

Principle #3Local quality

2Adaptability or versatility

If conventional compression standards with fixed bit depth are used for HDR images, then compatibility with existing infrastructure is maintained, but the representation of HDR content becomes suboptimal

Engineering Contradiction:

ImprovecompatibilityVSAvoidHDR representation accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent transforms HDR image parameters from high-precision floating-point to lower-precision fixed-point format through perceptual quantization, enabling compatibility with conventional compression standards while preserving perceptual quality through intelligent bit allocation

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent introduces perceptual quantization as an intermediary transformation step between HDR content creation and conventional compression encoding. This intermediary process converts HDR data into a format that works seamlessly with existing infrastructure while maintaining visual fidelity

Inventive Principle:

Principle #24Intermediary (Mediator)

3Device complexity

If uniform quantization is applied to HDR images, then the processing complexity is reduced, but visual fidelity deteriorates due to insufficient bit allocation in different luminance regions

Engineering Contradiction:

Improveprocessing complexityVSAvoidvisual fidelity

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent implements content-adaptive quantization that applies different quantization parameters to different regions of the image based on local content characteristics. Smooth regions receive finer quantization while textured regions receive coarser quantization, optimizing visual fidelity without excessive complexity

Inventive Principle:

Principle #3Local quality

4Measurement precision

If high bit depth encoding is used for HDR images, then the dynamic range representation is improved, but the compression efficiency decreases

Engineering Contradiction:

Improvedynamic range representationVSAvoidcompression efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent applies perceptual quantization to change the bit depth parameters dynamically based on content characteristics, achieving efficient compression by allocating bits where they provide the most perceptual benefit rather than using uniform high bit depth throughout

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS10701359B2Real-time content-adaptive perceptual quantizer for high dynamic range images

Publication Date: 2020.06.30 DOLBY LABORATORIES LICENSING CORP

AI summary

Noise levels in pre-reshaped codewords of a pre-reshaped bit depth in pre-reshaped images within a time window of a scene are calculated. Per-bin minimal bit depth values are computed for pre-reshaped codeword bins based on the calculated noise levels in the pre-reshaped codewords. Each per-bin minimal bit depth value corresponds to a minimal bit depth value for a respective pre-reshaped codeword bin. A specific codeword mapping function for a specific pre-reshaped image in the pre-reshaped image is generated based on the pre-reshaped bit depth, the per-bin minimal bit depth values, and a target bit depth smaller than the pre-reshaped bit depth. The specific codeword mapping function is applied to specific pre-reshaped codewords of the specific pre-reshaped image to generate specific target codewords of the target bit depth for a specific output image.