Perceptual Importance Maps for Image Encoding Efficiency
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Solution Overview
Problem
Current image processing techniques lack the ability to accurately determine the relative importance of different image portions to the human visual system, which is crucial for efficient encoding, region identification, and selective rendering.
Innovation Solution
The method involves calculating cost values for image blocks to determine motion vectors, deriving confidence values, and modifying them to reflect perceptual importance, generating a perceptual importance map that allocates bits, identifies regions of interest, and adjusts rendering detail based on human visual system sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform bit allocation is used for image encoding, then encoding simplicity is maintained, but encoding efficiency and quality deteriorate due to inability to prioritize important regions
Solution Approach 1:
The patent applies local quality by transitioning from uniform bit allocation to non-uniform bit allocation based on perceptual importance maps. Different regions of the image are assigned different bit rates according to their perceptual importance, allowing high-importance regions to receive more bits for quality preservation while low-importance regions receive fewer bits, thereby optimizing encoding efficiency without requiring complete redesign of the encoding framework
2Productivity
If all image regions are processed with equal detail, then processing simplicity is maintained, but rendering efficiency deteriorates due to inability to optimize for human visual sensitivity
Solution Approach 1:
The patent implements local quality in rendering by using perceptual importance maps to differentiate processing intensity across image regions. High-perceptual-importance regions are rendered with higher detail and computational intensity, while low-importance regions are rendered with reduced detail, optimizing rendering efficiency by focusing computational resources where human vision is most sensitive
Solution Approach 2:
The patent applies segmentation by dividing the image into multiple regions based on perceptual importance. This allows the rendering system to process different regions with different levels of detail independently, improving overall rendering efficiency without requiring complete redesign of the rendering pipeline
3Measurement precision
If motion vector estimation uses all candidate vectors, then estimation accuracy is improved, but computational cost increases
Solution Approach 1:
The patent applies partial action by evaluating only a subset of candidate motion vectors rather than all possible candidates. By using perceptual importance maps to guide the search process, the system achieves sufficient motion vector accuracy for most regions while significantly reducing computational cost by avoiding exhaustive evaluation of all candidates, particularly in low-importance regions
Data Source
AI summary
The present disclosure is directed to techniques for determining a perceptual importance map. The perceptual importance map indicates the relative importance to the human visual system of different portions of an image. The techniques include obtaining cost values for the blocks of an image, where cost values are values used in determining motion vectors. For each block, a confidence value is derived from the cost values. The confidence value indicates the confidence with which the motion vector is believed to be correct. A perceptual importance value is determined based on the confidence value via one or more modifications to the confidence value to better reflect importance to the human visual system. The generated perceptual importance values can be used for various purposes such as allocating bits for encoding, identifying regions of interest, or selectively rendering portions of an image with greater or lesser detail based on relative perceptual importance.