Image Coding TRIZ Cases

Tiled point cloud encoding cuts latency and codec complexity while preserving service quality for VR and self-driving data streams.

Hybrid G-PCC and V-PCC with octree coding reduces point cloud encoding complexity and latency while preserving delivery quality.

Adaptive selection of encoder-decoder pairs improves neural video compression ratio while reducing SoC power and bandwidth demands.

Using attribute prediction mode as the entropy-coding context improves point cloud residual compression and transmission efficiency.

Three independent mesh subdivision steps enable parallel decoding of a base mesh, cutting processing time while preserving target definition.

Multiple neural encoders and decoders are selected by image region to improve compression quality while limiting SoC footprint, power, and bandwidth.

Hyperprior-based probability and enhancement parameters improve neural image decoding quality while reducing complexity in reconstructed blocks.

Sparsification and tensor decomposition shrink machine-vision feature maps to cut latency, bandwidth, and cloud compute while preserving privacy.

Iteration-wise distortion data guides vertex counts in 3D mesh reconstruction, cutting unnecessary subdivision while preserving rendering quality.

A unified latent-space transformer replaces problem-specific neural operators, enabling scalable spatio-temporal simulation across meshes and particles.

Multiple reference frames and neural feature prediction improve dynamic point cloud compression while limiting motion-analysis complexity.

Only necessary point cloud attribute frames are coded over time, reducing data volume and processing load while preserving needed image quality.

Separate luma and chroma kernel selection improves video block decoding efficiency, bitrate allocation, and decoded fidelity across frame sections.

Adding predictor indexes to mesh residual contexts separates geometry and attribute residuals for more efficient bitstream encoding.

Separate luma and chroma transform kernels improve block decoding flexibility and compression across varied frame partitions.

Caching non-interactive content at edge servers cuts bandwidth and congestion, reducing latency for interactive streaming and spectator sharing.

Edge priorities combine error metrics and encoding cost, while vertex split prediction cuts redundant connectivity data in progressive mesh compression.

Compressed multimedia is split into bandwidth-fit streams so satellite links can deliver messages from out-of-network areas without excessive delay.

A base mesh plus layered displacement decoding cuts 3D mesh throughput and latency while preserving scalable reconstruction quality.

A multi-branch entropy network uses 1x1 and 3x3 group convolution with local attention to improve probability estimation and compression efficiency.

Selective face vertex generation in octree TriSoup decoding cuts point cloud bitstream volume and processing load without losing needed geometry.

By encoding one vertex from the difference to two others' combined displacement vectors, this case cuts 3D mesh data volume and decoding load.

Mesh-tension maps and aggregated wrinkle textures generate realistic facial wrinkles for unseen expressions with less manual work.

Combining text prompts with 2D and 3D encoders plus SDS loss improves 3D object diversity, resolution, and realism from limited 3D data.

Different encoding parameters across mesh patches improve compression while boundary adjustment prevents cracks in reconstructed 3D meshes.

Patch contour extraction and hierarchical mesh encoding cut volumetric video bitrate while preserving dynamic mesh borders and reducing artifacts.

A dual-encoder neural renderer edits face attributes from real images, avoiding hand labeling and complex 3D asset pipelines.

Selective decoding of feature-map enhancement layers helps an NPU balance image analysis quality, bandwidth use, and processing time.

Bitstream profile signaling lets a picture codec switch decoder networks to match device computing power and balance delay with compression.

Adaptive predictor subsets and ordering by mesh and face type improve mesh attribute prediction while reducing bits for predictor signaling.

Fused Y and UV feature processing cuts decompression compute while improving reconstructed image performance through optimized channel management.

Separate ALF, scaling list, and LMCS APS units cut redundant parameter coding and reduce network, memory, and decoder processing use.

CNN-based centroid-normal prediction cuts 4D XR mesh bandwidth and memory use by sending a low-res mesh plus geometry residuals.

Collaborative downsampling and upsampling uses low- and high-frequency point cloud features to cut bit rates while preserving reconstruction quality.

A generative decoder separates face motion and background data to cut distortion while preserving image quality in synthesized face video.

Backend tessellation converts infrastructure geometry into tile content that supports polylines and edges while reducing vertex duplication.

Priority-ranked latent channels let an autoencoder reconstruct images at usable quality even when bandwidth limits prevent full data delivery.

Profile-based encoder and decoder selection balances compression, delay, and computing load across low-power and high-power devices.

Joint Y and UV feature fusion cuts decompression compute while preserving reconstruction performance through flexible channel management.

Pixel ray crossing refines MPI layer geometry to avoid segmentation distortion and improve random viewpoint rendering quality.

Adaptive CNN in-loop filtering improves video coding by reducing artifacts at block boundaries and handling chroma subsampling more effectively.

Bitstream indicators define candidate motion vector list length in subblock merge mode, improving video encoding and decoding efficiency.

Chart-based texture prediction compresses dynamic mesh coordinates with entropy-coded residuals, cutting data volume for real-time 3D capture.

Significance maps and layered point cloud compression cut transmission latency and decoding load while preserving quality for VR and self-driving.

A neural material mapped onto coarse geometry preserves 3D asset detail while cutting rendering and storage demands for mobile and web use.

Temporal-level G-PCC tracks improve point cloud access and reconstruction, reducing latency and playback complexity for scalable delivery.

Feedback-guided point cloud bitstream coding reduces encoding complexity and latency while preserving service quality for VR and self-driving use.

Vector-quantized sub-primitive presence data cuts shader executions in ray tracing, reducing latency and power while preserving image quality.

Hierarchical subspace encoding with dual identifiers cuts point cloud decoding load while preserving efficient 3D data reconstruction.

Adaptive reference sample lines with padding and filtering improve intra prediction efficiency while lowering image data volume and coding cost.