Digital Video Signal Modification TRIZ Cases

Screening reference samples for current-block color prediction lowers computation and memory bandwidth while avoiding outlier-driven errors.

Matching pursuits, PLQ, and MERGE coding compress motion-rich video data while preserving visual quality and reducing transmitted data.

Adaptive Rice parameter mapping decodes residual coefficients more efficiently, cutting video data volume while preserving HD and UHD image quality.

Dense matrix fusion and decoupled training cut intra prediction inefficiencies while preserving coding performance and tool compatibility.

Extrapolation-based intra prediction improves signaling, transform, and chroma coding by adapting luma and chroma prediction to local video content.

Frequency-ordered intra prediction mode lists cut signaling overhead while preserving prediction accuracy in video encoding and decoding.

Filtered reference pixel subsets cut video color prediction complexity and memory bandwidth while avoiding exceptional samples that hurt accuracy.

Fractional-pel block vectors improve IBC and IntraTMP prediction for natural and screen content, raising video coding efficiency.

Using IBC geometric partitioning and two block vectors, this case improves video coding efficiency while preserving compression quality.

Cross-component residual modeling improves intra, IBC, and chroma block conversion by raising coding efficiency without excessive model complexity.

Shared filter parameters across picture regions cut repeated parsing, lowering in-loop filtering latency and decoder overhead.

Interleaved decoding units and fallback timing control cut end-to-end video delay while preserving compatibility with legacy decoders.

Predefined reference picture list allocation and slice-header index signaling improve VVC-style encoding efficiency while simplifying decoder memory handling.

Bilateral matching refines affine control point motion vectors to improve video block reconstruction quality while using bits more efficiently.

A single-slice-per-subpicture flag cuts redundant layout syntax from the bitstream, reducing decoding data, memory use, and bandwidth.

Confirmed-frame referencing cuts bandwidth and latency while keeping dropped or corrupted video frames decodable for time-sensitive transmission.

Selecting a smaller reference pixel subset removes outliers, cutting video coding complexity and memory bandwidth while improving color prediction accuracy.

Interleaved NAL units and selective layer signaling cut inter-view decoding delay while preserving multi-view coding efficiency.

Affine candidate selection and subblock motion compensation improve prediction accuracy for rotation and zoom in image encoding.

Gradient-corrected bi-prediction uses picture-interval weighting to improve motion-compensated prediction accuracy, coding efficiency, and image quality.

Neighbor-based prediction and entropy-coded residues shrink 3D LUT transmission data while preserving accurate color transforms in video decoding.

Type information embedded in VCM bitstreams lets decoders select task-specific optimization methods, improving AI image coding efficiency.

Fusing reconstructed coding and reference frame information cuts accumulated inter-encoding errors and improves temporal stability in video compression.

Rectangular reference regions and derived filter coefficients improve intra prediction by using reconstructed and predicted samples more efficiently.

Combining two intra-prediction modes by rate-distortion analysis improves motion prediction, cuts blocking artifacts, and lowers video data needs.

Zeroing high-frequency residual coefficients cuts 32-point and 64-point LGT matrix cost while preserving coding efficiency for video compression.

Combining intra template matching and specific intra modes improves prediction accuracy while cutting bitstream size for image encoding and decoding.

Block-level filter candidates tied to motion vector resolution improve prediction quality while cutting signaling and decoding complexity.

Using an initial motion vector from a correlated reference block enables earlier prediction, cutting video coding delay and complexity.

Selective use of non-separable transforms on smaller blocks improves image coding efficiency while limiting decoding complexity on larger blocks.

Prior-frame error metrics guide quantization for later interframes, limiting visual distortion while keeping video data requirements low.

Extending MMVD and SMVD beyond translational motion lets bi-prediction coding support affine and temporal models with better compression efficiency.

By moving weighted prediction table signaling between picture and slice headers, this case cuts overhead while preserving video coding efficiency.

Pixel-level weight mapping from surrounding references improves prediction accuracy and coding efficiency for non-rectangular video blocks.

Non-overlapping scaling intervals cut adaptive quantization and latent scaling complexity while preserving entropy coding performance.

Dual-model chroma prediction applies linear and nonlinear mapping by pixel correlation type to cut residuals and improve video compression.

Selective NSPT and LFNST improve block-based image coding efficiency while limiting complexity across different block sizes.

Weighted blending of subblock inter prediction and block-level intra prediction improves video coding efficiency as data volume rises.

Differential coding of INR network parameters across video frames cuts bitstream size and decoding complexity by reusing prior-frame weights.

Model-based prediction cuts signaling bits by exploiting luma, chroma, and neighbor-sample redundancy to improve image encoding efficiency.

Log-domain variance indexing and probability table lookup cut variance adjustment cost in AI image compression encoding and decoding.

Adaptive motion vector predictors and resolution selection improve video compression while reducing encoding and decoding complexity.

Selective channel-block activation matches image content and compression rate to cut neural encoding complexity while preserving reconstruction quality.

CCLM derives chroma prediction from neighboring luma and chroma samples to improve image coding efficiency and cut transmission and storage costs.

Affine motion prediction gains compression efficiency by deriving local illumination correction parameters from motion vectors with no extra bitstream overhead.

Flexible prediction groups with different modes let picture decoding run in parallel, improving prediction efficiency and coding speed.

Boundary blocks are recursively split into sub-blocks so image encoding can handle uneven image sizes and improve compression efficiency.

Hierarchical APS signaling cuts scaling list overhead in video and image coding while preserving visual quality and compression efficiency.

Optimized VPS signaling maps shared DPB and PTL syntax structures to output layer sets, cutting bitstream overhead in high-resolution video.

Non-adjacent block scanning expands affine merge candidates, then prunes redundancy to improve motion prediction and coding efficiency.