Digital Video Signal Modification TRIZ Cases

This case selects filter lengths by prediction angle, smoothing acute-angle references while limiting codec complexity.

Separate triangular sub-blocks use distinct motion information to improve prediction and reduce coding residuals.

This image coding case reorders MMVD candidates using motion differences to improve prediction and reduce motion-information bit overhead.

SPS-signaled HRD parameters use temporal identifiers and operation points to target video bitstream conformance tests efficiently.

MPM list refinements reduce bits for less likely intra prediction directions.

Gradient-based BDOF motion refinement improves prediction samples and supports efficient high-quality image bitstream transmission.

Dynamic candidate lists combine angular, DC, planar, and IBC modes to improve coding accuracy for smooth video content.

Chroma upsampling aligns subsampled video with luma resolution before neural encoding and decoding, preserving spatial correlation.

This case combines TIMD, WAIP substitution, and SATD-based weighting to improve prediction while limiting mode-testing complexity.

Clipped CCALF coefficients combine luma and chroma filtering to improve image quality while reducing video coding resource use.

This case uses encoder, hyper-encoder, decoder, and discriminator networks to improve high-dimensional image compression and reconstruction.

Sequential quantization passes improve transform coefficient coding while limiting complexity.

Intra-mode secondary transforms improve video compression efficiency for high-quality content.

This case uses LFNST and tree-aware scaling lists to improve chroma quantization efficiency and reduce transmission and storage costs.

Segment clustering selects bitrate points for better video quality and lower resource use.

Conditional syntax signalling selects predefined video block partitions, reducing decision complexity and syntax overhead.

This case configures separate intra prediction modes for square and non-square blocks to improve video coding efficiency.

Adaptive quantization balances bitstream size and reconstructed image quality.

This case adapts reference samples, transform splits, and prediction directions to improve coding for non-square blocks.

Recursive subdivision of boundary blocks handles non-multiple image sizes while using neighboring data to improve compression.

Removing partition inputs simplifies machine learning video filtering for lower hardware complexity and maintained compression performance.

Adaptive merge candidates and template costs support geometric partitioning while reducing computational complexity and pipeline latency.

This case applies DIMD, TIMD, IntraTMP, or SGPM from luma blocks to chroma blocks under Direct Mode for efficient decoding.

This case uses coefficient-region significance to parse MTS indexes and improve compression efficiency for high-resolution media.

Adaptive LFNST parsing uses block conditions and color-component flags to improve coding efficiency.

Motion vector resolution and neighbor-block selection improve prediction accuracy while reducing differential vector data in bitstreams.

Adaptive SAO band offsets improve reconstruction while limiting transmitted data.

Pre-generated 3D model data and availability checks reduce wait time and prevent failed virtual-viewpoint content generation.

Context coding of MTS index bins selects transform kernel sets, improving image compression efficiency while managing coding complexity.

A signaled syntax element lets decoders choose output layers, balancing decoding flexibility, processing load, and circuit scale.

A video decoder uses predicted and delta QP values to adjust chroma independently, reducing syntax overhead and decoded-video artifacts.

Bidirectional prediction and reordered candidate lists improve accuracy while reducing signaling bits in image coding.

This case selectively converts chroma blocks, adjusts and clips quantization parameters, and supports high-resolution bitstream handling.

This case adapts encoding points through segment clustering and local-global analysis to reduce redundant encoding and quality gaps.

This case uses picture- and slice-layer reshaping parameters to limit HDR information loss and repeated encoding overhead.

Optical-flow-aware block sizing refines affine video motion prediction.

This decoding approach selects Rice tables for coefficient levels, improving residual coding across mixed low and high values.

Triangle and geometric video partitions use separate interpolation filters for each motion direction, improving coding efficiency.

Template matching scores candidate MVR pairs to improve compression while limiting processing complexity in bi-prediction and affine coding.

A shared Decoder Parameter Set spans coded video sequences, supporting coding efficiency and decoding resilience on lossy channels.

Restricting IBC searches to IBC- or palette-decoded blocks reduces encoder load while preserving coding quality.

This case uses subpicture indices and SEI constraints to improve message association, extraction, and video bitstream conformance.

This case separates luma and chroma CABAC contexts to improve probability adaptation for VVC coded block flags.

Adaptive CBF context selection improves VVC coding efficiency and lowers implementation cost.

This case encodes directional, distance-based delta vectors around a base vector to improve accuracy without full motion re-estimation.

Anti-diagonal line buffers interleave prior coefficients to reduce non-contiguous access and speed entropy decoding.

The case uses scanning, grouped coefficients, and unary or Golomb-Rice coding to reduce redundant calculations during video decoding.

This case selects reference-sample templates by coding-unit position to improve CCCM chroma prediction and reduce bitstream size.

A CNN reallocates MPI depth layers around scene objects, reducing empty slices, memory footprint, and foreground blur.

This case uses tile indices, slice dimensions, and difference values to improve high-definition video encoding and decoding efficiency.