Geometric Image Transformation TRIZ Cases

A shared image stream enables real-time material sorting at reduced resolution while neural-network analysis runs on higher-resolution data.

Association tags in grouped point-cloud and panoramic media files let unpacking devices decode only relevant items, improving speed.

Projection-format decoding reconfigures intra prediction modes to handle massive 360-degree image data with better compression efficiency.

Content-aware rotation checks image type before reorienting the screen, avoiding disruptive auto-rotation for temporary ads.

MPM reconfiguration and projection-based reconstruction improve 360-degree image decoding efficiency for large VR and AR image data.

Selective boundary expansion and recursive block splitting improve 360-degree image compression and decoding across multiple projection formats.

Separate left and right optical paths widen image spacing to create a convergence angle for 3D ophthalmic viewing without ocular lenses.

High-resolution image data is downscaled for a discrete display chip, then restored by the driver IC to preserve compatibility and display quality.

Uses object and peripheral-object information to detect hidden search targets in captured video and improve missed detections.

Spatial mode sorting and heterodyne detection recover sub-wavelength field information for passive super-resolution imaging without sample interaction.

Automatic foreground, background, and 2D sticker compositing cuts manual drawing work while improving animation generation efficiency.

A single GPU kernel reuses cached input pixels for multiple downscaling outputs, reducing DDR bandwidth use and CPU overhead.

Octree and sub-octree bitstream segmentation cuts point cloud encoding and decoding latency while preserving service quality for VR and self-driving.

Recursive quad, binary, and triple tree partitioning improves 360-degree image compression and reconstruction for high-resolution VR and AR content.

Tree-based block division improves 360-degree image encoding and decoding by adapting partitions to projection formats and reducing processing burden.

Server-side image synthesis from stored scene views enables free viewpoint changes in VR while sending only one encoded stream with low latency.

A skip flag controls when transform pictures are referenced in inter prediction, improving compression efficiency while reducing decoding complexity.

Shared floating diffusion lets pixels with different exposure times read within one frame, cutting HDR noise while preserving frame rate.

Dedicated ISP-to-video and display buses bypass intermediary memory, cutting image-transfer latency and power with backpressure flow control.

Different SR processes are assigned to video sub-regions to improve compression efficiency and quality without uniform up-sampling.

Aligned moiré and reconstructed images with direction data cut manual comparison time and improve molded product defect evaluation accuracy.

Low-resolution, low-frame-rate rendering paired with AI upscaling and frame supplement cuts repeated 3D animation re-rendering time.

Recursive quad, binary, and triple block partitioning improves 360-degree image compression across ERP, CubeMap, OctaHedron, and IcoSahedral formats.

Adaptive block division and projection-based reconstruction improve 360-degree image compression while managing decoding complexity.

A pre-trained CNN predicts encoder settings from each input image, avoiding iterative trials while meeting quality and compression targets.

Adaptive block division and projection-based reconstruction improve 360-degree image decoding efficiency under massive data loads.

Guided room mapping builds shared XR spaces with local and remote previews, preserving spatial context and reducing occlusion errors.

Edge-aware blending and green-guided signal adjustment recover spatial resolution in color separation lens array imaging while reducing blur and color mixing.

User-selected constraint inputs guide image stylization to improve output quality and give more reliable style control.

Adaptive Laplace perturbations protect private input features during neural network inference while preserving accuracy in cloud-based use.

Pre-generated and stored augmented M2M data expands limited AI training sets while avoiding added complexity during model training.

Scaling offsets and projection formats improve inter prediction, helping decode high-resolution 360-degree images with less data burden.

Projection-aware prediction and reconstruction cut 360-degree image data load while preserving high-resolution VR and AR media quality.

Offset-based scaling and projection-aware prediction cut 360-degree image data loads while preserving high-resolution VR and AR decoding.

Independent map scales for full-screen and window layouts preserve visible map area during in-vehicle navigation mode switching.

Adaptive tree-based block splits and residual reconstruction improve compression and decoding of high-resolution 360-degree image data.

Projection-based inter prediction reconstructs 360-degree images across cube map and octahedron formats to improve compression and decoding efficiency.

Partitioned projection decoding combines predicted and residual images to compress 360-degree VR and AR image data more efficiently.

Projection-specific prediction and reference picture expansion improve 360-degree image compression while managing VR and AR data volume.

Projection-based decoding reconstructs 360-degree images with format-specific expansion and inter prediction to improve compression efficiency.

Scaling offsets guide inter prediction across ERP, CMP, and OHP projections to compress high-volume 360-degree VR and AR image data.

Projection-specific scaling, image expansion, and motion prediction improve compression of high-resolution 360-degree images for VR and AR.

Region-wise packing and projection conversion improve 360-degree image compression while limiting decoding load and processing time.

Selective face-region expansion and projection-specific prediction improve 360-degree image decoding and compression with lower processing burden.

Multimodal artifact vectors reuse tags from similar documents, cutting compute load while keeping metadata tagging practical at scale.

Multiple 2D CT views are identified and back-projected into 3D to improve complex object recognition without slowing security screening.

Partitioned 360-degree image coding uses motion-vector prediction and adaptive regions to cut data volume while preserving immersive visual detail.