Digital Video Signal Modification TRIZ Cases

Subpicture-aware boundary selection improves prediction while reducing the data needed to transmit and store high-quality images.

This case uses AR device capability levels to guide split-rendering negotiation, reducing network and server processing overhead.

Weighted averages of neighboring vertex motion vectors improve V-DMC mesh compression, reducing bandwidth while maintaining visual quality.

Chroma-fusion neural loop filtering selects the lowest rate-distortion mode to improve video quality and reduce bandwidth pressure.

Conditional temporal-sublayer signaling improves video compression and picture quality.

Block-size-based weighting candidates expand multi-hypothesis prediction while limiting overhead bits in video coding.

SMVD and short-term references improve inter-prediction coding efficiency.

A WCDAB in-loop CNN uses multi-input frames, QP maps, and separable convolutions to enhance video quality efficiently.

Converting partially decompressed media syntax elements into compact tokens reduces data volume for generative AI training and inference.

The case partitions coding blocks by residual distribution, selectively transforms sub-blocks, and infers other residuals as zero.

This case uses luma classifiers and mapping tables to select chroma offsets, improving quality while managing bitrate.

Refined syntax activates neural post-filters by slice, tile, or region to adapt video coding to varying local content.

TLV types identify reference data units, reducing decoder storage while shift operations simplify spherical scale-offset computation.

This case uses prediction-mode rules and non-separable transforms to improve video coding accuracy while limiting complexity.

Neighboring reconstructed samples and reference blocks improve intra prediction while limiting boundary effects and bitstream overhead.

This case applies matrix-based intra prediction by block type to reduce redundant signaling while preserving visual quality.

This case conditionally signals chrominance QP offsets and mapping data to reduce bitstream overhead while preserving picture quality.

Frame-level models lose local noise detail; sub-image in-loop generation restores region-specific patterns during decoding.

This video coding case segments predictor candidates, reorders computed-cost entries, and limits redundant processing to improve efficiency.

Block transforms and entropy coding compress sparse 3D effects for fast decoding.

This case derives chroma quantization tables and parameters to preserve reconstructed image quality while reducing transmitted data.

Selective storage of sample data and ALF or CABAC state data supports pruning and random access without memory-state mismatches.

This case derives candidate motion vectors from summed reference-picture vectors to improve inter prediction coding efficiency.

Fixed-length clipping signaling simplifies adaptive loop filtering, reducing coding overhead while preserving picture quality.

Separate luma and chroma splitting trees adapt to local content, while luma-based context formation improves chroma entropy coding.

This video decoding approach selects transform matrices from intra prediction modes to improve coding efficiency without a full redesign.

This case compares decoded and original video in a safe part to verify non-safe encoding while avoiding loop-filter non-determinism.

A constant-slope allocator uses chunk complexity and feedback to globally optimize bitrate versus visual quality in distributed encoding.

In-loop resampling applies adaptive filters across picture tiles to reduce buffer demands while preserving quality during bandwidth shifts.

This video decoding case uses threshold-based candidate selection to improve motion vector prediction and reduce coded bits.

This image decoding case uses compact resolution-to-layer mapping to reduce negotiation data and streamline bitstream processing.

Selective HMVP buffer initialization and updates support parallel motion derivation while improving compression efficiency.

This case uses adaptive sub-block filters and reconstructed luma samples to improve chroma prediction while limiting image data volume.

This case uses output and reference-layer rules to remove unnecessary layers while preserving required bitstream scalability.

This case selectively disables decoder tools in lossless regions to preserve perfect reconstruction while reducing bit rate and complexity.

Using top-left and bottom-right tile IDs to define slice boundaries reduces header overhead, memory use, and decoding workload.

Adaptive deblocking selects block-size-specific filtering in QTBT video decoding to preserve image quality and support parallel processing.

Multitree subdivision shares coding parameters across leaf blocks to reduce signaling overhead.

Filtering reference samples reduces prediction errors in stereographic video decoding.

This case uses VUI metadata to signal output picture dimensions, balancing display flexibility with director-guided resolution.

SEI messaging sends essential luminance and chromaticity data, reducing overhead for HDR and WCG display management.

Spatial and affine motion candidates are ranked by rate distortion cost to improve compression in inter-coded video blocks.

This case applies adaptive secondary transform cores to smaller coefficient blocks, improving compression while managing coding complexity.

Split horizontal and vertical KLT stages use adaptive matrices to reduce codec complexity while preserving compression efficiency.

This case uses motion vector differences and reference picture types to improve video compression while reducing coding complexity.

Boundary-strength control and parallel filter engines reduce blocking artifacts, processing load, and bit rate for ultra-HD decoding.

This image coding case aligns dependent slices with LCU rows to reduce dependencies, transmission delays, and processing complexity.

CIIP-aware merge signaling removes unnecessary syntax while preserving prediction reconstruction, improving video compression efficiency.

Video encoding and decoding select one or multiple upper reference lines by block position, balancing prediction accuracy with buffer size.

Selective motion candidate table updates support accurate video prediction while reducing encoding complexity and bandwidth demands.