Image Coding TRIZ Cases

Dividing point-cloud space into encoded subspaces and sharing identifiers in control information helps reduce decoder processing load.

Hybrid bonding connects image-sensor and memory chips for analog inference on-chip, reducing data transfer for image compression.

Frequency-domain transforms and recursive bit-plane coding produce fixed-size packets for image and texture data, reducing bandwidth demands.

Integer quantization replaces floating-point transformer operations in ResNet filters, reducing video coding complexity.

Point cloud slices use multi-branch partitioning and indication data to avoid non-single-point flags, reducing encoding and decoding bitrate.

Octree segmentation compresses geometry and attribute data while reducing point-cloud transmission latency and encoding complexity.

Fixed filters limit spatial redundancy removal in intra prediction; neural networks adapt reference pixels and weights for more accurate image block coding.

An adaptive flag selects single- or multi-point leaves in N-ary trees to improve 3D point cloud coding efficiency and reduce processing.

A unified Morton code lookup table reduces storage and computational complexity in G-PCC point-cloud encoding and decoding.

Manipulated reference sub-pictures support subsequent sub-picture decoding, helping multiview 3D video coding manage boundaries and prediction leaks.

History-based candidates join spatial candidates from neighboring blocks to lower inter-prediction processing load while maintaining coding efficiency.

Adaptive quantization signaling omits unnecessary change data when parameters match, reducing point-cloud bitstream size and improving coding efficiency.

Octree and sub-octree segmentation supports parallel encoding and selective decoding of point clouds to reduce latency.

Combining AMVP and merge modes around shared neighboring candidates reduces processed information while preserving video quality.

Type-specific headers and slices let decoders skip unnecessary point cloud units, reducing decode time and computational cost.

See how positional local self-attention uses neighborhood memory blocks to replace spatial convolutions, reducing computation and parameter requirements.

Tree-structured attribute subgroups and multiple levels of detail organize point cloud data for parallel encoding, lowering complexity and transmission latency.

The case uses data types, parameter ranges, and size limits during 3D primitive generation to reduce content size without distortion.

Non-rectangular video blocks are split for adaptive intra or inter prediction, improving bit efficiency and reducing edge discontinuities.

GPU rendering and adaptive session scaling address setup complexity, latency, and limited scalability in interactive 3D video streaming.

Metadata signals receptive-field sizes and allowable patch margins so neural-network inference stays consistent across video encoders and decoders.

Layered point cloud bitstreams deliver essential service quality first, then enhancement data, helping reduce latency during transmission.

Preset checks on prediction mode and coding-unit size select transform kernels for IBC, improving video coding efficiency while reducing signaling bits.

Reverse CLERS traversal removes virtual points and table restarts, simplifying triangular-mesh decoding and improving cache use.

Traditional CT compression can lose information across applications; trained models adapt detector data for efficient, low-loss reconstruction.

A serialized CSRLE stream lets multiple planar decoders share image data, reducing buffering, memory use, and bandwidth during rendering.

Changing mesh connectivity complicates real-time compression; neighboring vertex errors guide temporal prediction residues.

Z-ordering removes unused bitmap bits from tracked mipmap tiles, reducing storage and computational load during graphics rendering.

Rounding errors make HEVC and VVC tile sizes unpredictable; a fixed remainder pattern keeps boundaries consistent without recalculation.

Patch-wise geometry and attribute coding addresses point-cloud latency and complexity for VR, AR, MR, and self-driving services.

Combining G-PCC octree coding with V-PCC prediction helps reduce point cloud transmission latency and encoding complexity for VR and self-driving services.

Different arithmetic contexts encode residuals from inter or intra prediction, improving compression efficiency for large 3D point-cloud datasets.

A reusable neural network compresses and decompresses multiple graphics textures, reducing hardware circuit, bandwidth, and storage demands.

Neural processing circuits decompress compressed graphics textures, reducing dedicated hardware needs, silicon area, and energy use.

Derived mesh triangulation predicts 3D vertices while preserving connectivity with a compact representation for efficient transmission.

Atlas sampling maps dynamic mesh occupancy into codec-friendly images to compress changing connectivity and attribute maps for real-time AR and VR.

CU-level I_PCM selection skips unnecessary processing in image regions using non-compression encoding, supporting real-time efficiency.

Combining entropy coding with zero-run encoding preserves lossless video compression while reducing encoding complexity and latency.

Adaptive context-based coding uses point-cloud attribute residuals to exploit redundant information and improve compression efficiency.

Traditional QR codes can disrupt social-network visuals; style transfer reshapes encoded images while preserving bit-string decoding.

Layering point-cloud attributes by spatial region lets encoding and decoding use reference and difference parameters for better coding efficiency.

Cartesian reference geometry lets point-cloud encoders use closer peripheral points, limiting the efficiency loss of polar coordinates.

3D motion vectors align and resample volumetric frames from separate sub-streams, reducing judder in immersive 6DoF rendering.

A two-encoder neural renderer edits facial attributes from real images, preserving photorealism across varied parameter combinations.

A perceptual encoder uses neural networks and user opinion models to adapt QP per frame, preserving quality while cutting bitrate.

This case uses shifted coordinate groups to select reference points and improve point cloud attribute prediction efficiency.

Depth, spatial, and azimuth selection adapts point cloud prediction to discontinuous scenes, improving accuracy and coding efficiency.

A multilayer perceptron compresses radar cubes before storage and reconstructs them for target detection with lower memory demand.

This video coding case uses reconstructed luma and coding parameters in separate embeddings to improve chroma quality with lower data needs.

A tree-based structure applies different quantization steps to isolated and non-isolated points, reducing storage and transmission time.