Deep Image Interpolation Using Accumulation Curves
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Solution Overview
Problem
Existing methods for interpolating pixel data in images with depth information often introduce artifacts and complicate the editing process, particularly when resizing or transitioning between images, as they fail to maintain the structural integrity of deep images.
Innovation Solution
A computer-implemented method for interpolating image data with depth information involves obtaining image datasets, determining accumulation curves, generating interpolated accumulation curves, and forming interpolated pixel image value arrays to create a modified deep image dataset, while parsing and truncating pixel samples based on object identifiers to maintain structural integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional pixel interpolation methods are used on deep images, then interpolation can be performed, but artifacts are introduced and structural integrity is compromised
Solution Approach 1:
The patent segments the deep image into multiple depth layers, each representing a specific depth range. By processing each layer separately through the interpolation pipeline (including separate accumulation curve generation and blending operations), the method maintains the structural integrity of each depth layer while enabling interpolation. This segmentation prevents artifacts that would occur if all depth information were treated as a single unified structure.
Solution Approach 2:
The patent introduces accumulation curves as an intermediary representation between the original pixel data and the interpolated result. Instead of directly interpolating pixel values across depth layers, the method first converts pixel data into accumulation curves that encode depth information, performs interpolation on these curves, and then converts back to pixel values. This intermediary approach preserves structural integrity while enabling smooth interpolation transitions.
2Device complexity
If deep image data is processed without depth-aware methods, then processing is simpler, but depth-related artifacts are introduced
Solution Approach 1:
The patent applies local quality by treating different depth regions with specialized processing appropriate to their characteristics. Each depth layer is processed individually with depth-aware operations (such as depth-range-based blending and layer-specific accumulation curve generation), ensuring that artifacts are avoided in depth-critical regions while maintaining overall image quality. This localized depth-aware processing prevents the depth artifacts that would result from uniform processing.
3Productivity
If pixel samples are not parsed by object identifiers, then processing is faster, but editing capability and structural maintenance are reduced
Solution Approach 1:
The patent performs preliminary action by parsing pixel samples according to object identifiers during the initial processing stage, before interpolation and rendering operations. This pre-organization of pixel data by object ID enables efficient editing operations later (such as selective object manipulation) without requiring re-processing of the entire image. The preliminary parsing maintains processing speed by using efficient data structures while enabling enhanced editing capability through object-level access.
Data Source
AI summary
An image dataset comprising pixel depth arrays might be processed by an interpolator, wherein interpolation is based on pixel samples. Input pixels to be interpolated from and an interpolated pixel might comprise deep pixels, each represented with a list of samples. Accumulation curves might be generated from each input pixel, weights applied, and accumulation curves combined to form an interpolation accumulation curve. An interpolated deep pixel can be derived from the interpolation accumulation curve, taking into account zero-depth samples as needed. Samples might represent color values of pixels.