Geometric Image Transformation TRIZ Cases

Grouped coefficient coding predicts GCLI values and entropy-encodes residuals to cut meta-data while preserving quantized data quality.

Selective luminance-only or full color encoding cuts image distortion and hardware cost while preserving temporal noise reduction.

Splitting transform coefficients into set and symbol indexes improves point cloud compression when neighboring samples are too sparse for prediction.

Optical masks compress image data during capture, cutting processing load and power use while preserving reconstructable image information.

Additional patches encode missed 3D points in video-based point cloud compression, reducing cracks and holes in reconstructed views.

Distance-weighted bin classes and adaptive local templates improve entropy context selection, cutting bits for transformed video coefficients.

Physical diffractive masks compress image data before sensing, cutting processing load and power use while preserving useful image quality.

Occupancy-guided patch filtering compresses point cloud spatial and attribute data for faster transmission and accurate reconstruction.

Grouped coefficient coding predicts GCLI residues and keeps selected bit planes to cut meta-data while preserving compressed data quality.

Physical mask arrays filter light before sensing, cutting image data and processing power while preserving useful information for analysis.

Optical masks filter image data during capture, cutting processing load and power use while preserving useful sensing coefficients.

KD-tree block division and graph kernel tuning reduce sub-graph issues in point cloud attribute compression and support parallel processing.