Image Coding TRIZ Cases

Game metadata guides encoding choices for video sequences, helping reduce latency, bandwidth requirements, and visual-quality tradeoffs.

This case layers geometry and attribute data for selective decoding, reducing complexity and latency in point-cloud transmission.

This case predicts added mesh vertices from decoded neighbors, supporting layered compression and efficient 3D content transmission.

Pre-simulated heterogeneous meshes are quantized and cached to reduce runtime computation and memory use during fluid rendering.

Chunked batches enable contrastive learning with larger effective batches under GPU memory limits.

Normalize UV coordinates to match texture map sizes and improve 3D mesh reconstruction.

Separate geometry and color quantization parameters, plus their difference, improve point cloud compression and decoding.

V3C mesh patches encode geometry and connectivity through projection, raw, embedded, or external vertex-position coding.

Patch images and PCCNAL units compress point clouds for faster streaming.

Dynamic padding adapts neural in-loop filtering to varied video slices.

Distribution-aware quantization improves feature map use in neural video coding.

The method sends essential point cloud geometry first, then refinement data, reducing latency and complexity for VR and self-driving.

This case replaces complex displacement transformations with signed distance codes for more efficient polygon mesh compression.

Planar flags and angular context coding target inefficient geometry compression in directional, non-natural point clouds.

This case uses planar mode flags and context-adaptive coding to reduce redundant occupancy bits in octree point cloud compression.

Control and attribute identification information helps decode point cloud attributes accurately while reducing transmission data volume.

Signals neural post-filter data through video coding syntax for quality control.

Neighbor-based prediction mode selection reduces encoding and decoding workload for attribute-rich point cloud data.

A cross-bar ReRAM array stores image dictionaries and processes patches in parallel to reduce power and circuit space.

This case uses G-PCC temporal scalability group boxes to organize tracks, reducing mixing and supporting efficient point cloud services.

This case separates base meshes from quantized displacement fields for hardware-compatible decoding of dynamic 3D content.

This case groups attribute information and applies local quantization parameters to improve point cloud encoding and transmission.

This case uses depth-based layers and optional Morton ordering to compress 3D point attributes with lower processing load.

This point cloud coding case uses precomputed RAHT neighbor indices and early termination to reduce search complexity and coding time.

Azimuthal contexts and inferred direct coding reduce tree-splitting overhead for directional LiDAR point cloud compression.

This case selects DCT or DST per image block to balance bit rate, distortion, and added transform complexity.

Selective frequency-coefficient encoding allocates bits by gaze position, reducing transmission load while preserving image quality in VR.

This case inserts padding frames in a decoder cache queue to reduce cloud gaming delays on limited user terminals.

Boundary-aware triangulation compresses dynamic mesh connectivity and reconstructs UV coordinates.

This case selects associated child-node neighbours to reduce LUTs and calculations while improving octree encoding speed and accuracy.

This case maps 3D unit vectors to 2D sphere coordinates and adaptively avoids distorted regions during compression.

Using seven associated octree nodes instead of 26 reduces LUT use and calculation demands while improving intra prediction accuracy.

Clients select fidelity for indexed volumetric units, reducing 3D bandwidth and rendering load while preserving visual quality.

This case groups samples and carries parameter sets across tracks to support temporal scalability and efficient G-PCC bitstream access.

Azimuthal angles and capturing-laser data improve isolated-point identification and entropy coding context selection for LIDAR point clouds.

A bounded LOD sampling period keeps point cloud encoding and decoding consistent while improving processing efficiency.

Wavelet scattering, deep learning, and selective encoding compress distribution-network images to reduce memory and communication overhead.

This decoding approach updates visited-vertex neighborhoods to reduce topological configurations and split vertices in 3D mesh bitstreams.

Reference frames predict current attributes before slicing, enabling residue coding with improved compression efficiency and accuracy.

Uniform parent regions share presence states, reducing child data and shader workload during ray-tracing intersection tests.

Independent luma and chroma partitioning trees optimize transform blocks, reducing redundancy and bandwidth in video decoding.

A distortion and embedding-aware loss produces compact biometric representations for recognition and model retraining without raw images.

This case selectively applies parent-node prediction in N-ary point-cloud trees to improve compression while limiting decoding complexity.

Geometry and video-based compression with layered bitstreams supports efficient, lower-latency point cloud services for VR and self-driving.

Parallelogram-grid predictors reduce 3D mesh data intensity and signaling.

Signed, invisible watermarks help verify capture sources and media integrity when real-time deepfake detection is unreliable.

This case applies weight-based RAHT step sizes and QP offsets to improve point cloud compression and decoding accuracy.

Local plane and occupancy data cut point cloud context storage by 86%.

Bitstream encoding uses segmented processing and preliminary actions to reduce latency while preserving point cloud service quality.

Neighboring cuboid occupancy and edge positions guide entropy coding of vertex flags for more efficient point cloud transmission.