Image Coding TRIZ Cases

A 3DoF+ encoding method selects a reference view to convey viewport-dependent light effects for consistent rendering.

A selective predictor calculates multiple prediction vectors to identify the closest match for each vertex in a shape traversal.

A quantized auto-encoder neural network compresses image data using flexible encoder architecture and fake quantization during training.

A graphics texture encoding method selects partitioning patterns for texel blocks using predefined reference sets.

Signaling frame-specific attribute parameters in point cloud bitstreams resolves the trade-off between compression efficiency and parameter flexibility.

Normalizing depthmaps with logarithmic scaling reduces bandwidth consumption while enabling generic client decoders without proprietary software.

Hierarchical bitstream organization enables partial data access for 3D scenes, resolving bandwidth constraints while maintaining reconstruction quality.

A 3D point cloud encoder processes attribute data by calculating difference values between current and predicted points to generate compact code sequences.

Block management information enables selective decoding of 3D structures, reducing processing load by avoiding full Octree reconstruction.

A variable Morton code encoding method incorporates primitive size into the coding pattern to normalize spatial data.

Prediction type signaling and temporal order fields standardize point cloud coding bitstreams.

An encoder modifies the quantization stride using a target point weight to improve color prediction accuracy and reduce bit rate.

Deriving local illumination compensation parameters from mapped domain template blocks eliminates inverse mapping table loading, reducing decoding latency.

Latent diffusion probabilistic models resolve monocular depth ambiguity and occlusion inference challenges by operating in compressed representation spaces.

Directional dilation aligns texture onto mesh seams using vertex-based orientation data, eliminating black space and uneven feature positioning artifacts.

A training apparatus determines weighted losses based on neural network performance to enable coordinated learning across multiple models.

Meta-learning adjusts neural weights for smooth bitrate control, eliminating multiple model instances.

A common compression scheme processes pixel data across multimedia system components, reducing memory bandwidth and power consumption.

An inter-prediction Trisoup coding scheme encodes residuals from reference frames to enhance point cloud compression efficiency.

A point cloud decoder determines level of detail counts from bitstream syntax elements to reconstruct attribute information.

Partitioning reconstruction slices into independent entropy segments enables parallel decoding operations within video processing systems.

An importance function prioritizes perceptually critical 3D scene elements for adaptive resource allocation.

A method projects 3D point clouds onto 2D frames using standard video codecs to enable efficient data transmission.

Parallel block segmentation reduces encoding latency while distributing decoding complexity across independent regions.

Variable occlusion layers capture hidden background information, reducing image errors in layered 3D displays.

Octree segmentation reduces computational complexity and latency by enabling fast decoding of varying point cloud service qualities.

Tensor product B-spline models represent images as continuous functions to reduce computational complexity while maintaining high dynamic range image quality.

Segmenting point cloud data into tiles enables parallel encoding and decoding, reducing latency and complexity in large dataset processing.

Color space conversion derives alpha channel lower bounds to adjust predicted values, reducing residual data volume and bandwidth requirements.

A hybrid encoding system routes virtual desktop frame data to separate GPU and CPU paths.

Encoding method uses width maps to determine alpha values for blended pixels, reducing transmission costs while maintaining smooth object transitions.

A spherical video encoding method selects visible tiles based on view perspective and maps frames to two-dimensional representations.

Root nodes in a residual quad tree signal chroma presence, unifying luma and chroma coding to reduce encoder complexity.

Atlas packing auxiliary patches separates rendering and processing data to prevent dizziness during 6DoF navigation.

A video generating device assigns Laplace pyramid levels to compose 360° videos with heterogeneous spatial quality.

A progressive mesh encoder alternates between full-edge and half-edge collapse operators to generate multiple levels of detail.

A projection factor scales a reference block to generate an orthogonal scaled prediction data block for image encoding.

Neural image compression uses latent feature-domain intra-prediction to encode residuals between prediction blocks and original blocks.

A point cloud coding method determines attribute prediction values using occupied neighboring nodes and same-level child nodes before applying region adaptive hierarchal transform.

A point cloud transmission device encodes geometry information using octree sampling and occupancy code compression to support efficient data processing.

This approach filters higher frequency coefficients to reduce bandwidth requirements while preserving image quality for spacecraft transmission.

Encoding system selects point cloud prediction modes using image motion vectors, reducing iterative calculation requirements for autonomous vehicle sensing.

A difference detection unit associates neural network output values with encoding information to identify image variations.

A polar coordinate circle initializer structures neural network parameters to accelerate model convergence and reduce training energy consumption.

Predicting vertex split connectivity guides edge selection during progressive mesh encoding to improve data compression efficiency.

Thinning restart markers reduces address table size, enabling efficient decoding of specific image regions without loading full marker data.

Segmenting 3D video into consistent mesh sequences reduces data intensity, enabling real-time rendering of free-viewpoint content.

Scaling a reference block via a projection factor creates an orthogonal residual, reducing energy loss during quantization.