Image Coding TRIZ Cases

Pre-loaded hierarchical color and height maps reduce computational load, eliminating display delay during high-quality 3D rendering.

A point cloud transmission device encodes geometry and attribute data using octree-based entropy coding units to improve compression efficiency.

A pruned sparse voxel octree structures 3D surface data into block-oriented nodes for efficient multiresolution compression and decompression.

Segmenting point clouds into coarse base layers and fine enhancement features reduces computational expense while maintaining storage efficiency.

Dividing point cloud data into access units matching network MTU sizes resolves integration bottlenecks with streaming applications.

Adaptive non-linear filtering reduces prediction errors and code size compared to fixed linear methods.

A butterfly asymmetric discrete sine transform enables parallel computing via SIMD operations.

Subdividing texture blocks into varying subsets enhances image quality by reducing artifacts and noise across diverse color hues.

Merging deep image samples by primitive identifier reduces file sizes while preserving alpha transparency for compositing.

Sense element engagement coordinates electronic neuron interaction strengths to produce self-organized patterns of visual experience.

Embedding time and location data within transmitted images resolves the loss of sequential message information in standard multimedia services.

A point cloud encoding method transforms syntax element values by subtracting a constant offset to reduce bit consumption.

A semiconductor device determines a decoding area to decode small screen images within a whole image.

Assigns color weights to image elements for weighted codeword combinations, reducing memory bandwidth and processing power requirements.

G-PCC encoders predict color residuals via scaling factors, reducing bits needed while maintaining accuracy.

A 3D data encoding method parses syntax to determine prediction trees for inter or intra prediction.

Reversing the RDPCM scanning path eliminates value dependencies, allowing RDOQ application and improving encoding efficiency.

Independent sub-picture coded picture buffer removal times reduce codec delay while maintaining encoder-decoder synchronization.

Encoder neural networks generate latent variables to compress images, resolving the trade-off between file size and image quality.

A BVH compression method stores child bounding boxes as lower precision offsets from high precision parent coordinates.

An embedded codec circuitry applies variable length coding to zero-valued residuals and fixed length coding to non-zero values within image sub-blocks.

Encoding spherical harmonic function information on specific patches reduces data redundancy while maintaining 6DoF texture realism.

Merging separate context arrays into a single model lowers computational complexity while maintaining encoding precision.

A hierarchical group of frames structure organizes point cloud samples to enable inter prediction using multiple reference PC samples within the same group.

Detecting geometric features in point clouds enables efficient encoding through mathematical representation, resolving octree compression inefficiencies.

Adaptive vertex traversal orders polygon mesh encoding to reduce data stream size while lowering computational complexity.

Exponential quantization of partitioned bit sequences reduces storage volume and transmission time while maintaining measurement precision.

Parallel convolutional branches extract multi-scale features to resolve the rate-distortion tradeoff in deep learning image compression.

Multiplying a node QP offset by a geometry multiplier reduces signaling overhead while maintaining coding efficiency.

Adaptive layer-based search reduces processing amount by limiting neighbor lookups for distant points.

An image processing device reduces captured data volume by eliminating high frequency components in peripheral regions to maintain central visibility.

A portrait stylization framework blends latent codes to control image personalization and artistic style.

Feedback processing module configures graphics engine based on encoder metrics to prevent quality degradation from network bandwidth limitations.

Separating vertex and face degrees reduces variance, improving coding efficiency while managing complexity.

A video decoder stores a predefined set of reference pictures in its buffer to decode output images, enabling random access without decoding previous segments.

Inter-prediction compresses dynamic point clouds by modeling temporal relationships, reducing storage and transmission costs.

Reducing arithmetic coding contexts accelerates point cloud encoding hardware.

A valence-based traversal order minimizes prediction errors in triangular mesh data compression.

A prediction model estimates target pixels to generate residuals for multi-bitrate image compression.

Encoder merges planar mode eligibility into one flag, reducing bit usage in sparse point clouds.

A point cloud coding method uses inter-channel prediction to decorrelate color attributes via YCoCg-R transform.

Encoder-side innovations estimate reconstructed sample values within overlap areas to improve intra block copy prediction efficiency.

A single neural network compresses video data by removing output elements based on a determined compression rate.

Splitting shared vertices between textured polygons prevents graphic artifacts during decompression, maintaining visual quality while reducing data size.

Context modeling decodes mesh displacement prefixes to resolve encoding efficiency bottlenecks caused by syntax element dependencies.

A decoding method extracts split structures from hierarchical coding units to optimize compression efficiency.

A 3D data encoding method generates a data unit containing prediction trees and identification information to signal subsequent tree presence.

A dual serving scheme generates single rate decoded level of detail data with extra corner information to enable progressive refinement.

Encoder enforces a maximum bit count constraint on the bitstream so decoders verify compliance without analyzing content, reducing processing load.