Cascade Prediction for Video Codec Dynamic Range Adaptation
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Solution Overview
Problem
Existing video codecs struggle to efficiently support a wide variety of display devices with different dynamic ranges and color spaces, leading to suboptimal video quality and compatibility issues.
Innovation Solution
The implementation of cascade prediction methods that allow for the encoding of image data in one dynamic range and color space, with metadata enabling reconstruction and rendering in different dynamic ranges and color spaces, thereby supporting backward compatibility and improved video quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video codecs encode image data for a specific dynamic range and color space, then video quality is optimized for that specific display type, but compatibility with other display devices deteriorates
Solution Approach 1:
The patent segments the video data into multiple datasets, each optimized for different dynamic ranges (SDR, HDR10, HLG). The codec selectively encodes and transmits only the necessary segments based on the target display's capabilities, avoiding the need to transmit all possible variants while maintaining both quality optimization and broad compatibility.
Solution Approach 2:
The patent employs parameter changes by adjusting encoding parameters (dynamic range, color space, bit depth) according to the target display device's characteristics. The system modifies these parameters dynamically during encoding based on metadata about the intended display, thereby optimizing video quality for each specific display type without compromising overall compatibility.
2Adaptability or versatility
If multiple image datasets are encoded for different display devices, then compatibility across devices is improved, but data transmission efficiency deteriorates
Solution Approach 1:
The patent divides the complete video content into segmented datasets tailored for different display categories (SDR-only, HDR-capable, specific color spaces). By segmenting the data this way, the system transmits only the relevant segments needed for each display type, dramatically reducing the total data volume compared to transmitting all possible variants for every device.
Solution Approach 2:
The patent creates universal base datasets that can serve multiple display types, with optional supplemental datasets for specific enhancements. This multi-functional approach allows a single encoded dataset to serve SDR displays while another can enhance HDR-capable displays, reducing redundancy and overall data transmission requirements.
3Manufacturing precision
If video codecs are optimized for high dynamic range displays, then video quality for HDR displays is improved, but compatibility with standard dynamic range displays deteriorates
Solution Approach 1:
The patent segments video data into SDR-optimized datasets and HDR-optimized datasets. SDR displays receive and render only the SDR segment, ensuring perfect compatibility, while HDR-capable displays receive both segments and utilize the HDR portion for enhanced quality. This segmentation eliminates compatibility issues while preserving HDR advantages for supported displays.
Solution Approach 2:
The patent introduces metadata as an intermediary that carries information about display capabilities and data format. This metadata acts as a mediator between the encoded video data and the display device, enabling the system to automatically select and configure the appropriate dataset (SDR or HDR) based on the display's capabilities, thereby ensuring both compatibility and quality optimization.
Data Source
Figure 1A~1C
Figure 1D~1F
Figure 2A
AI summary
A first predictor is applied to an input image to generate first-stage predicted codewords approximating prediction target codewords of a prediction target image. Second-stage prediction target values are created by performing an inverse cascade operation on the prediction target codewords and the first-stage predicted codewords. A second predictor is applied to the input image to generate second-stage predicted values approximating the second-stage prediction target values. Multiple sets of cascade prediction coefficients are generated to comprise first and second sets of cascade prediction coefficients specifying the first and second predictors. The multiple sets of cascade prediction coefficients are encoded, in a video signal, as image metadata. The video signal is further encoded with the input image.