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.

Segmented history buffers let a decompression pipeline access recent and older data separately, improving Lempel-Ziv throughput without data loss.

Separation markers split variable-length coded image blocks so decoders can jump to needed regions faster without processing the full stream.

Separation markers split variable-length coded image blocks for direct partial decoding, cutting access delays on limited-capacity devices.

Selective interpolation converts dual fisheye regions into equidistant cylindrical stereo images while cutting processing time for live streaming.

An arrayed polarization imaging approach reduces repeated acquisitions while capturing full-field polarization and color data for real-time tissue differentiation.

Orientation-aware multi-resolution grid encoding improves 3D implicit representations by capturing geometric detail with smoother surfaces and fewer artifacts.

Wide-angle images are split into virtual camera views so existing vision models can detect and track objects without fisheye distortion or data loss.

Rendered simulation models create paired training images in different styles, improving personalized style transfer quality and variety.

Curved surface patches and 1D normal offsets compress high-resolution meshes for BVH building, cutting memory and ray tracing overhead.

Planar surfaces gain occupancy volumes and TSDF-based updates to keep AR object placement aligned with changing SLAM data.

Recursive block partitioning and projection-aware reconstruction improve 360-degree image decoding efficiency under massive VR and AR data loads.

Local facial region processing improves expression transformation quality while preserving accuracy and enabling special effects such as smiling or crying.

Grid-based CNN masking keeps concatenated object crops separate, preventing leakage while improving parallel re-identification and classification.

A movable magnified inset preserves wide-field situational awareness while improving target detail and point-of-aim viewing.

A low-resolution scene first locates the sign, then only that region is captured and enhanced for precise recognition with less data.

API call interception adds interpolated frames and paced output to keep display frame rates consistent when applications render irregularly.

Resampling uneven medical image intervals with interpolation supports accurate 3D modeling while avoiding extra scans and radiation exposure.

On-demand UAV imaging with deep learning improves landslide detection by combining wide-area coverage, high resolution, and faster response.

Depth-wise convolution reshapes demosaicing for NPUs, cutting memory-heavy operations and improving MAC unit utilization.

Synthetic instrument renderings switch perspective on trigger events to clarify surgical tool configuration during teleoperation.

Places graphics in 3D space using background analysis and display conditions to preserve creator intent and visual recognizability.

Fourier sublayers replace self-attention to fuse text and image embeddings with lower time and memory cost on limited hardware.

A neural network ISP replaces hand-tuned image blocks by learning raw patch reconstruction for demosaicing and noise reduction.

Feature and distribution correlation guide GAN-based image domain conversion to reduce content distortion and preserve image consistency.

Automatic cue-based magnification in passthrough headsets lets users read real-world text without removing the headset or switching to glasses.

Replacing overexposed or underexposed screen footage with synchronized source video preserves consistent picture quality in hybrid streams.

AI and computer vision detect game-critical assets and adapt mapped video frames so key elements stay visible across different screens.

Corrects skewed and distorted document images by aligning fixed features before OCR, reducing repeated searches and processing time.

Multiple wavelength-specific metalenses and pixel arrays combine low-resolution channel images into compact high-resolution capture.

Recursive block partitioning and projection-aware reconstruction improve 360-degree image compression for high-quality VR and AR decoding.

Projection-specific block reconstruction and reference image expansion improve 360-degree image compression and decoding efficiency.

Handles large 360-degree image data by decoding recursively split blocks across ERP, CubeMap, OctaHedron, and IcoSahedral formats.

Using time or phase differences from two sensors, tagged objects can be identified and positioned in images in real time on mobile devices.

Maps real rooms into shared XR with local and remote previews plus occlusion masks, preserving spatial context for collaboration.

Neural networks use frame size cues, optical flow, and blending to interpolate video frames with less compute and faster processing.

Different SR processes are assigned to video sub-regions to improve coding efficiency while balancing up-sampling quality and complexity.

A touch interface creates multiple independent map windows for localized zooming while maintaining the original display size of surrounding content areas.

Dynamic particle positioning generates continuous transitions without pre-created shapes, reducing memory usage and computational load.

A program adjusts virtual camera position and angle of view using displayed region size data and user-to-display distance measurements.

An eye tracking method normalizes scale and illumination in image data to fit an ellipse to the iris for gaze direction determination.

Nearest plane search reduces processing time while maintaining interpolated value reliability.

Downsampling back projection modules optimize feature maps via feedback loops, reducing compression artifacts and blurring while maintaining high quality.

An execution manager distributes application screens across electronic and external devices based on device information.

Optical apparatus combines high and low resolution image streams to create a variable-resolution display.

Cascaded muxer layers synthesize features to increase resolution by n×n times, reducing computation costs while maintaining minimal information loss.

Matrix multiplication-based interpolation removes bow-tie artifacts from VIIRS scans, reducing processing time to under 20 seconds on GPU devices.

A virtual image display device adjusts display image size based on measured user vision acuity.

Generative adversarial network processes portrait images and effect sequences to produce stitched special effect videos.

Gradient domain compositing calculates seam pixels via Poisson equations to eliminate visible artifacts while reducing computational load.