Image Coding TRIZ Cases

Independent body-part sub-graphs improve noisy motion reconstruction while keeping skeletal animation models smaller and more flexible.

Adaptive triangle extension in octree-based point cloud coding improves voxelization accuracy while limiting bitrate and complexity.

Bounding box volume and point count are constrained before node reconstruction, improving point cloud codec stability and robustness.

Integer adjustment factors replace floating-point scaling in neural visual coding to avoid device-specific errors and improve compression reliability.

A CNN trained on controlled image imperfections helps distinguish authentic information-bearing devices from high-fidelity counterfeits.

State-based CLERS bin assignments shrink mesh connectivity bitstreams and speed decoding in Edgebreaker-style coding.

Block-wise long-range context decoding uses corner-to-center latent prediction to cut complexity and improve neural image compression.

A volumetric autodecoder with normalized latent 3D diffusion learns view-consistent rigid and articulated assets from 2D images without 3D supervision.

Redundant edge rows and columns are compressed before decoding, cutting padding-related bandwidth waste and transmission latency.

Slice-based local symmetry planes and boundary vertex merging cut redundant mesh data and improve multi-plane compression efficiency.

A coordinate-based network converts geometry parameters into lower-correlation feature vectors to compress point cloud data more efficiently.

Transforms 3D mesh displacement vectors into YUV samples with more Y than U/V data to cut code amount while preserving reconstruction quality.

TriSoup edge and centroid vertex encoding in an octree improves decoded point cloud reproducibility on ridge lines and flat surfaces.

Spatial and history-based candidate lists cut inter prediction processing load while preserving picture coding efficiency.

Separating local and global coordinate data cuts decoder processing by avoiding real-time global coordinate calculation.

Neighboring cuboid occupancy and edge positions guide entropy coding of point cloud vertices, cutting geometry bitstream volume for real-time AR/VR.

Data rearrangement aligns reconstructed picture blocks with training-data distribution before neural filtering, improving coding and decoding results.

Multiple candidate depth lists handle point cloud discontinuities, reducing residuals and outliers while improving coding efficiency.

Distance-based point ordering replaces sparse octree traversal to cut point cloud encoding complexity while preserving decoding accuracy.

Prediction-specific numerical ranges switch entropy-coding contexts for 3D points, improving coding efficiency while limiting extra data handling.

A deep octree entropy model and block-based parallel compression reduce point cloud processing load, storage demand, and network traffic.

Extending HEIF with region mask structures, coding methods, and parameters improves media compression and decoding efficiency.

By isolating non-zero frequency blocks, this case cuts inverse transform calculations on zero coefficients and improves image coding efficiency.

A residual diffusion model refines codec reconstructions with adjustable sampling steps to improve low-bitrate image fidelity and perceptual quality.

Compresses volumetric neural network features with tensor decomposition and video coding while preserving spatiotemporal consistency.

Hierarchical encoding compresses sub-primitive presence data so ray tracing can avoid repeated shader execution, cutting latency and power use.

Non-rectangular block partitioning combines inter and intra prediction with adaptive edge filtering to improve video decoding efficiency and bit use.

Leaf flags let 3D point cloud encoders switch between single-point and multi-point N-ary leaves to improve compression and cut processing.

Prediction-specific arithmetic contexts encode inter and intra residuals separately to improve 3D point cloud coding efficiency with less data.

Subgroup-based point cloud decoding uses LoD generation and neighbor search to cut latency and complexity in geometry and attribute reconstruction.

Octree-level partitioning and combined G-PCC/V-PCC processing improve point cloud bitstream delivery with lower latency and manageable codec complexity.

Interpolated azimuthal angles improve point cloud geometry coding context selection, reducing arc tangent use and compression complexity.

Residual-radius updates drive dynamic predictors that compress sparse spinning-sensor point clouds with lower bit cost and latency.

Multiple seed points generate independent meshlets with fewer unique vertices, reducing rendering overhead in virtual-space mesh processing.

Threshold flags for mesh displacement coefficients improve 3D arithmetic coding efficiency while preserving high-quality decoding.

Precomputed depth atlases and metadata improve 6DoF depth estimation accuracy while reducing rendering complexity and playback delay.

Low-variance depth and color regions are downscaled in a depth atlas, cutting bit rate and pixel use while preserving volumetric view reconstruction.

Data is embedded in artificial fingerprint minutiae so partial damage and tampering are harder to defeat while standard imaging devices can still read it.

Precomputed heterogeneous mesh sequences cut fluid data complexity, enabling realistic splashes and splatter in real time on consumer devices.

Offset signaling and delta coding reduce lifting-transform bias in dynamic mesh compression while preserving accurate reconstruction.

Adaptive prediction lists combine depth, position, and azimuth cues to cut point cloud attribute residuals, outliers, and coding overhead.

Game metadata and rendering buffers estimate frame complexity early, enabling faster encoding choices with lower latency and stable visual quality.

Control information and attribute identifiers let compressed 3D point data decode accurately while reducing bitstream volume.

LoD subgrouping and neighbor search cut point cloud decoding complexity and latency while preserving geometry and attribute quality.

Game metadata and intermediate rendering buffers predict frame complexity so encoders can adjust bitrate and quality with lower latency and bandwidth.

Primary and secondary quantization positions compress fractional-coordinate point clouds with lower bit use and accurate reconstruction.

A centroid-normal prediction model encodes low-res meshes plus residuals to cut XR geometry bandwidth and compute load while preserving fidelity.

Independent kernel selection for luma and chroma improves video block compression and lowers decoding cost in high-resolution formats.

Weighted inter-intra prediction improves image compression by combining block modes based on neighboring blocks and predefined intra modes.