SDR to HDR Image Rendering via Bit Depth Expansion
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Solution Overview
Problem
The high cost of HDR cameras limits their accessibility to consumers, resulting in standard dynamic range (SDR) cameras being used for 360° video creation, which can lead to loss of details when stitching images together to form HDR content.
Innovation Solution
An electronic device and method for creating and rendering High Dynamic Range (HDR) images by increasing the bit depth of SDR images, normalizing them, stitching them together, and applying frame packing techniques to generate HDR content, allowing SDR devices to simulate HDR images based on user viewpoint metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HDR cameras are used to capture images, then image quality and dynamic range are improved, but device cost increases significantly
Solution Approach 1:
The patent creates a virtual copy of HDR image data by processing multiple SDR images through bit depth expansion and tone mapping. Instead of requiring expensive HDR cameras, the system generates synthetic HDR data from readily available SDR camera inputs, preserving detail information through computational methods rather than direct optical capture.
Solution Approach 2:
The patent transforms SDR images into HDR representations by changing the bit depth parameter from 8-bit to 10-bit or higher, and by applying tone mapping functions that expand the luminance range. This parameter transformation allows standard cameras to produce HDR-quality output through post-processing rather than requiring specialized hardware.
2Ease of manufacture
If SDR cameras are used for 360° video creation, then device cost is reduced, but image detail and quality are lost during stitching
Solution Approach 1:
The patent applies bit depth expansion and normalization procedures to SDR images before the stitching operation. By pre-processing the images to expand bit depth and normalize luminance values, the system prepares the data in advance to prevent information loss during the subsequent stitching process, ensuring that detail is preserved when multiple images are combined.
Solution Approach 2:
The patent adds a bit depth dimension to the image data, transforming 8-bit SDR images into 10-bit or higher HDR representations. This dimensional expansion in the data space allows for greater luminance precision and detail preservation during stitching, effectively adding a quality dimension without changing the physical camera hardware.
3Ease of manufacture
If multiple SDR images are stitched together to create 360° content, then device cost is reduced, but dynamic range and detail in dark and bright regions are compromised
Solution Approach 1:
The patent applies bit depth expansion (from 8-bit to 10-bit or higher) and luminance normalization to SDR images before stitching. These parameter changes enable the combined image to represent a wider illumination intensity range, preserving details in both dark and bright regions that would otherwise be lost in standard SDR processing.
Solution Approach 2:
The patent introduces intermediate processing steps including bit depth expansion, normalization, and tone mapping functions that act as mediators between the SDR input images and the final HDR output. These intermediary processes transform the limited dynamic range of SDR images into the extended dynamic range required for high-quality HDR 360° content.
Data Source
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AI summary
An electronic device includes a receiver receives a compressed bitstream and metadata. The electronic device also includes at least one processor that generates an HDR image by decoding the compressed bitstream, identifies viewpoint information based on an orientation of the electronic device, maps the HDR image onto a surface, and renders a portion of the HDR image based on the metadata and the viewpoint information. A display displays the portion of the HDR image.