Deblocking Filter With Local Signal Adaptation

The patent applies different filtering strengths (weak vs strong filtering) based on local signal characteristics. The filter decision unit determines whether to apply weak or strong filtering by evaluating local conditions such as pixel value differences and gradient magnitudes at block boundaries. This local adaptation allows the filter to effectively remove blocking artifacts while preserving important high-frequency information and maintaining good low-pass characteristics.

The patent introduces dynamic filtering where the filter strength is not fixed but adapts based on local signal properties. The filter decision unit dynamically selects between weak and strong filtering modes by evaluating conditions such as |p0-q0| > β and gradient calculations. This dynamic approach enables the filter to respond to varying local structures, effectively removing blocking artifacts while maintaining signal fidelity and achieving good low-pass characteristics.

The patent applies filtering strength locally based on signal characteristics. Weak filtering is applied where signal details are important, while strong filtering is applied only where blocking artifacts dominate. This selective approach removes artifacts effectively while minimizing information loss in regions containing important signal information.

The patent dynamically adjusts filter strength based on local conditions evaluated by the filter decision unit. By using criteria such as pixel value differences and gradient calculations, the filter adapts its strength to local needs, applying strong filtering only when necessary for artifact removal and weak filtering when signal preservation is more important, thereby optimizing the trade-off between artifact reduction and information loss.

The patent implements local adaptation by evaluating signal characteristics at each block boundary and selectively applying weak or strong filtering. The filter decision unit examines local properties such as pixel value differences, gradient magnitudes, and signal patterns to determine the appropriate filtering strength. This local quality approach enables the filter to adapt to diverse local signal structures including edges, textures, and smooth regions.

The patent introduces dynamic adaptation where the filter behavior changes based on real-time evaluation of local signal properties. The filter decision unit dynamically selects between weak and strong filtering modes using criteria that respond to local structures. This dynamic adaptation allows the filter to effectively handle various local signal characteristics, achieving optimal artifact reduction while preserving signal fidelity.