Adaptive Perceptual Mapping for HDR Video Coding
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Solution Overview
Problem
Current video encoding and decoding schemes, such as MPEG-4 AVC and HEVC, are not designed to handle High Dynamic Range (HDR) and Wide Color Gamut (WCG) video effectively, leading to poor quantization and perceptible noise due to fixed coding transfer functions that do not adapt to the content of the video.
Innovation Solution
A method is introduced that uses a perceptual quantizer function, defined by PQ(L) = c1 + c2 * (L^m1) / (1 + c3 * (L^m1) + m2), where parameters c1, c2, c3, and m1 have fixed values, and m2 is variable, to adapt the quantization based on luminance value ranges, allowing for adaptive perceptual mapping and reverse mapping operations to minimize noise and distortion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a fixed coding transfer function is used to convert linear input values into non-linear values, then the encoding process is simple and consistent, but the quantization allocation is poor leading to perceptible noise and distortion
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed coding transfer function to an adaptive one that changes based on input video characteristics. The system dynamically adjusts the transfer function parameters (particularly m2) according to the luminance value ranges and content properties of the input video, enabling optimal quantization allocation for different scenes while maintaining manageable complexity through parameter adaptation rather than complete system redesign
Solution Approach 2:
The patent implements parameter changes by modifying the coding transfer function parameters based on input video characteristics. Specifically, the system analyzes the luminance value ranges in the input video and adjusts parameters like m2 in the transfer function equation to optimize the mapping for the actual content being encoded, thereby improving quantization precision without requiring a completely different encoding architecture
2Manufacturing precision
If HDR video with broad luminance range is quantized using fixed coding transfer function, then encoding is straightforward, but details in shadows or highlights are lost
Solution Approach 1:
The patent applies local quality by analyzing different luminance value ranges within the HDR video content and applying optimized transfer function parameters specifically tailored to each range. The system identifies whether the content is primarily in shadow regions, highlight regions, or mid-tone regions, and adjusts the coding transfer function accordingly to preserve details in the dominant luminance range while maintaining overall HDR capability
Solution Approach 2:
The system dynamically adapts the coding transfer function to match the luminance characteristics of the input HDR video. By continuously analyzing the input video's luminance distribution and adjusting the transfer function parameters in real-time, the system maintains high adaptability to different luminance ranges while preserving fine details across the entire HDR spectrum
3Ease of operation
If uniform quantization is applied to non-linear values from fixed transfer function, then the quantization process is simple, but contouring or banding is perceived by viewers
Solution Approach 1:
The patent changes the transfer function parameters based on the actual luminance range present in the input video. By adjusting parameters like m2 according to the content characteristics, the system optimizes the non-linear mapping before uniform quantization, ensuring that the simple uniform quantization process produces visually acceptable results without contouring or banding artifacts
4Manufacturing precision
If coding transfer function does not consider human visual system sensitivity, then the encoding process is simpler, but perceptible distortion occurs
Solution Approach 1:
The patent incorporates human visual system characteristics by adjusting the coding transfer function parameters based on the luminance range and content type. The system uses psychophysical principles to optimize the mapping, changing parameters to match human sensitivity characteristics for different brightness levels, thereby improving perceptual accuracy while maintaining reasonable encoding complexity through parameter adaptation
Data Source
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AI summary
A method is provided for encoding a digital video to improve perceptual quality. The method includes receiving a digital video at a video encoder, providing a perceptual quantizer function defined by Formula (I); wherein L is a luminance value, c1, c2, c3, and m1 are parameters with fixed values, and m2 is a parameter with a variable value, adapting the perceptual quantizer function by adjusting the value of the m2 parameter based on different luminance value ranges found within a coding level of the digital video, encoding the digital video into a bitstream using, in part, the perceptual quantizer function, transmitting the bitstream to a decoder, and transmitting the value of the m2 parameter to the decoder for each luminance value range in the coding level.