Video Decoding Filtering Method for Blocking Artifact Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video compression techniques, such as H.264/AVC and HEVC, face inefficiencies in encoding and decoding due to increasing video size, resolution, and frame rate, necessitating improved encoding and decoding methods to enhance image quality and reduce hardware resource usage.
Innovation Solution
A filtering method that determines block boundaries for filtering in a reconstructed image, sets boundary strength based on block type and preset conditions, and applies filtering accordingly, using a video decoding apparatus with a boundary determination unit, strength setting unit, and filtering execution unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data is compressed using existing techniques (H.264/AVC, HEVC), then the volume of video data is reduced, but blocking artifacts occur and image quality deteriorates
Solution Approach 1:
The patent applies different filtering strengths to different block boundaries based on local characteristics. Boundary strength is determined by analyzing pixel differences across boundaries and applying stronger filtering where blocking artifacts are more severe, while using weaker filtering where boundaries are less problematic. This localized approach removes blocking artifacts effectively without uniformly degrading image quality across the entire video.
Solution Approach 2:
The patent dynamically adjusts filtering parameters (boundary strength values) based on local image characteristics and block types. Instead of using fixed filtering strength, the system modifies filtering parameters adaptively according to pixel differences, block sizes, and boundary positions, thereby optimizing the balance between artifact removal and image quality preservation.
2Object-affected harmful factors
If filtering is applied to remove blocking artifacts, then image quality improves, but encoding and decoding complexity increases
Solution Approach 1:
The patent segments the video into blocks and further divides boundaries into different types (strong boundaries, weak boundaries, invisible boundaries) based on local characteristics. This segmentation allows the filtering process to focus computational resources only on boundaries that actually require filtering, rather than applying complex filtering algorithms to all boundaries uniformly, thereby reducing overall encoding and decoding complexity.
Solution Approach 2:
The patent applies filtering selectively rather than universally. By identifying and filtering only the boundaries that exhibit blocking artifacts (partial action), the system avoids the excessive computational cost of applying strong filtering to all boundaries. The conditional filtering approach ensures that filtering operations are performed only where necessary, optimizing the trade-off between artifact removal and computational complexity.
Data Source
AI summary
A filtering method comprises: determining at least one block boundary to which filtering is to be applied in a reconstructed image; setting a boundary strength of the block boundary based on prediction modes of two target luma blocks forming the block boundary among a plurality of prediction modes, a type of the block boundary and preset conditions; calculating an average value of quantization parameters which are respectively applied to the target luma blocks, and deriving a variable by adding the average value to an offset value derived based on offset information to be encoded at a level of a sequence parameter set that is a header which is referenced in common by the pictures belonging to the sequence; and determining whether to perform the filtering on the block boundary and performing the filtering based on the set boundary strength and the variable.