Image Coding TRIZ Cases

A neural network selects probability models based on tree node levels to drive entropy coding of point cloud data.

A hierarchical transfer learning mechanism adapts central deep learning models to specific edge device characteristics through cloud-based segmentation.

A 3D data decoding method merges attribute information into sub-blocks of predetermined lengths compliant with point cloud compression standards.

A paletted encoding subsystem processes four-byte groups with five-bit headers to compress RGBA8 streams efficiently.

A lossless image compression method removes ancillary and redundant information from picture data before applying optimized parameters.

Context-based encoding of vertex mesh displacement fields reduces bitrate requirements for 3D content transmission without specialized hardware.

Segmenting texel blocks by pattern reduces computational complexity while maintaining accurate texture representation.

Storing decimation transforms reduces file size while maintaining visual quality during dynamic rendering.

A decoding method specifies curved surfaces to approximate three-dimensional points for high-fidelity reconstruction.

Segmenting pixel data into constant color subspans reduces memory bandwidth usage while improving compression ratios across 3D and media content.

Formatting LiDAR point clouds as raster frames enables lossless compression, reducing storage and bandwidth needs while maintaining data integrity.

Assigning triangle-based models to dense areas and tree-based models to sparse regions improves compression efficiency while managing coding complexity.

Quad-tree-binary-tree partitioning adapts to bounding box symmetry for flexible point cloud data structure handling.

Separating base meshes from displacement sub-bitstreams reduces transmission time while preserving spatial fidelity in dynamic 3D content.

Packing union patch occupancy maps into a global map reduces bitstream transmission overheads for large 3D sensor data.

Mapping 3D data to 2D surfaces resolves compression inefficiencies in dynamic scenes.

Varying luminance in adjacent segments embeds machine-readable information without altering the original image or consuming additional surface area.

A display encoder module transmits visual data to multiple remote monitors across a network using independent frame buffers.

Selective reference to specific base layer areas reduces memory access workload, suppressing encoding overhead while maintaining information completeness.

A transcoding method maps source image quality parameters to target formats using dynamic configuration adjustments.

Encoder compresses point cloud attribute data using fixed-point arithmetic to reduce storage needs while maintaining quality.

Encoding positional data in a predetermined sequence aligns output with attribute processing, eliminating sorting overhead during decoding.

An octree structure encodes point cloud data to reduce encoding complexity and latency in VR services.

Segmenting point clouds into isolation and non-isolation modes reduces bitrate while maintaining encoding speed.

Discretized motion model generates accurate human sequences via unsupervised learning, eliminating manual labeling requirements.

An encoding apparatus calculates predicted points based on measurement model information to reduce data volume.

Region-adaptive hierarchical transform partitions separate coefficients to resolve scalability bottlenecks in spatial, temporal, and quality dimensions.

Tiling mesh and texture data allows selective decoding, reducing computational load on consumer devices.

Dynamic bit allocation optimizes memory storage while maintaining image throughput.

A 3D data encoding method stores control information within data units to identify geometry or attribute types for efficient extraction.

A motion compensation method projects 3D surface patches onto a 2D canvas for efficient processing.

Adapting arithmetic coding to angular characteristics reduces point cloud data volume while preserving spatial precision for autonomous driving applications.

Projection geometry adaptation reduces data size while maintaining quality during volumetric video encoding.

A block-based differential compression method records air traffic control graphic data streams by capturing only changed pixel regions.

Scaling high dynamic range image data into a low dynamic range format reduces memory requirements and bandwidth while maintaining visual quality.

A host renderer decouples artificial reality scene graphs from hardware rendering calls to improve compatibility.

Segment palette pixels into groups based on spatial position to minimize distance errors and improve compression efficiency.

Merging transform unit flags with coefficients in one traversal eliminates separate storage needs, reducing memory usage while maintaining coding flexibility.

A file generating apparatus aligns random access points across component streams using synchronized decoding and composition timestamps.

Machine learning model segments video regions using motion and texture data to optimize coding parameters.

Reduces memory volume and access frequency in 3D graphics systems by extracting essential depth information through tile-based plane identification.

A decoder generates multiple compensation data sets from loss data to decompress images with reduced average offset.

Transforming quantization parameters through non-linear functions reduces computational complexity in video encoding while maintaining coding efficiency.

Unified syntax merges V-PCC and MIV methods to compress diverse 3D content in a single bitstream without separate coding systems.

Sorting laser angles before encoding eliminates decoder-side sorting operations, reducing decoding time and complexity.

A hybrid projection encoding apparatus segments point clouds into visible and occluded regions to compress 3D geometrical representations efficiently.

A tessellated primitive index compression method stores domain point indices and references to reduce memory usage.

Component coordinate systems normalize vertex orientation in 3D meshes, reducing quantization errors on large flat surfaces and enhancing visual quality.

Post-process decoded mesh patches by splitting edges and inserting vertices to generate new triangles aligned with the original surface.

A neural network video filter reduces computational load by applying 1x1 convolutions to sparse supplementary data inputs.