Adaptive Digital Image Noise Reduction via Per-Pixel Filtering
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Solution Overview
Problem
Existing video compression techniques, such as block-based compression standards like AVC/H.264, introduce artifacts like block noise and mosquito noise into digital images, which can degrade image quality, especially in low bit rate data and large display formats.
Innovation Solution
A method and apparatus that determine mosquito and block noise on a per-pixel basis, using adaptive filters to reduce compression artifacts by combining filter outputs based on noise values, employing edge adaptive filtering to preserve true image features and selectively apply filtering to areas with noise, thereby improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If block-based compression standards like AVC/H.264 are used to compress video content, then storage area needed and transmission time are reduced, but block noise and mosquito noise artifacts are introduced into the image data
Solution Approach 1:
The image is divided into blocks corresponding to the compression standard's block structure. Noise detection and filtering operations are performed independently on each block, allowing selective processing of noisy regions while preserving quality in clean regions.
Solution Approach 2:
Different filtering strengths and types are applied to different regions of the image based on local noise characteristics. The filtering operation adapts its parameters locally to reduce artifacts in noisy areas while maintaining image quality in areas without artifacts.
2Object-generated harmful factors
If strong filtering is applied to reduce compression artifacts, then noise is reduced, but image details and true features are blurred or lost
Solution Approach 1:
The filtering operation is made dynamic and adaptive rather than static. Filtering parameters such as strength, type, and application region are adjusted dynamically based on detected noise characteristics in different areas of the image, allowing the filter to be strong where needed and gentle where not needed.
Solution Approach 2:
The filtering strength and type vary locally across the image based on detected artifact presence. Regions with compression artifacts receive stronger filtering while regions without artifacts receive minimal or no filtering, preserving true image features in clean regions.
3Object-generated harmful factors
If traditional noise reduction methods are applied uniformly across the entire image, then overall noise is reduced, but non-noisy areas are unnecessarily smoothed
Solution Approach 1:
The image is segmented into regions with artifacts and regions without artifacts through block-level noise detection. This segmentation allows the filtering operation to be selectively applied only to noisy regions, leaving clean regions untouched.
Solution Approach 2:
The filtering operation adapts its behavior locally across different image regions. Some regions receive strong filtering while others receive no filtering, based on the presence or absence of compression artifacts in each region.
Data Source
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AI summary
Devices, systems, methods, and other embodiments associated with reducing digital image noise are described, in one embodiment, a method includes determining, on a per pixel basis, mosquito noise values associated with pixels of a digital image. The method determines, on a per pixel basis, block noise values associated with the digital image. The method filters the digital image with a plurality of adaptive filters. A compression artifact in the digital image is reduced. The compression artifact is reduced by combining filter outputs from the plurality of adaptive filters. The filter outputs are combined based, at least in part, on the mosquito noise values and the block noise values.