Bit Depth Enhancement via Edge-Aware Area Segmentation
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Solution Overview
Problem
Digital images and videos stored at low bit depths often exhibit visual artifacts like 'banding' or 'false contouring' when displayed on high-resolution and high-contrast devices, due to inadequate color representation, and increasing bit depth increases storage and transmission requirements.
Innovation Solution
A method involving multiple iterations of bit depth enhancement operations, where images are divided into areas, edge detection is performed to identify areas without edge features, and a blur is applied to those areas, dynamically determining the blur size to increase bit depth without losing edge features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If images are stored and transmitted at higher bit depth, then visual artifacts like banding are eliminated and color representation is improved, but storage space and data transmission requirements increase
Solution Approach 1:
The image is divided into multiple areas based on gradient magnitude calculations. Different processing operations are applied to different areas: smooth gradient areas receive bit depth enhancement through blur operations, while areas with edges or high-frequency content are preserved at original bit depth. This segmentation allows selective application of bit depth enhancement only where needed, reducing overall storage requirements while improving visual quality in problematic areas.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the image. Smooth gradient areas that exhibit banding artifacts receive full bit depth enhancement processing, while areas with edges, textures, or high-frequency content are processed at lower quality or skipped entirely. This local quality approach ensures that storage space is consumed only where visual improvement is actually needed, rather than uniformly across the entire image.
2Measurement precision
If images are stored and transmitted at higher bit depth, then visual artifacts like banding are eliminated and color representation is improved, but data transmission requirements increase
Solution Approach 1:
The image is divided into multiple areas based on gradient magnitude calculations. Different processing operations are applied to different areas: smooth gradient areas receive bit depth enhancement through blur operations, while areas with edges or high-frequency content are preserved at original bit depth. This segmentation allows selective application of bit depth enhancement only where needed, reducing overall storage requirements while improving visual quality in problematic areas.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the image. Smooth gradient areas that exhibit banding artifacts receive full bit depth enhancement processing, while areas with edges, textures, or high-frequency content are processed at lower quality or skipped entirely. This local quality approach ensures that storage space is consumed only where visual improvement is actually needed, rather than uniformly across the entire image.
3Measurement precision
If bit depth enhancement is applied to all areas of an image, then visual artifacts are reduced, but edge features may be lost or degraded
Solution Approach 1:
The image is divided into multiple areas based on gradient magnitude calculations. Different processing operations are applied to different areas: smooth gradient areas receive bit depth enhancement through blur operations, while areas with edges or high-frequency content are preserved at original bit depth. This segmentation allows selective application of bit depth enhancement only where needed, reducing overall storage requirements while improving visual quality in problematic areas.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the image. Smooth gradient areas that exhibit banding artifacts receive full bit depth enhancement processing, while areas with edges, textures, or high-frequency content are processed at lower quality or skipped entirely. This local quality approach ensures that storage space is consumed only where visual improvement is actually needed, rather than uniformly across the entire image.
Data Source
AI summary
A method for processing an image having a first bit depth includes performing two or more iterations of a bit depth enhancement operation that increases the bit depth of the image to a second bit depth that is higher than the first bit depth. The bit depth enhancement operation includes dividing the image into a plurality of areas, performing an edge detection operation to identify one or more areas from the plurality of areas that do not contain edge features, and applying a blur to the one or more areas from the plurality of areas that do not contain edge features. In a first iteration of the of the bit depth enhancement operation, the plurality of areas includes a first number of areas, and the number of areas included in the plurality of areas decreases with each subsequent iteration of the bit depth enhancement operation.