Image Coding TRIZ Cases

Viewpoint looping information structures enable precise playback control in omnidirectional media applications.

Switches reference sample filters by aspect ratio to improve prediction accuracy for elongate blocks without increasing computational complexity.

A point cloud transmission device encodes geometry and attribute data using inter-prediction with reference regions.

Segmenting point cloud data into tiles enables parallel encoding and decoding, reducing latency and complexity in high-efficiency services.

Dynamic output delay synchronization reduces buffer memory size while maintaining image quality during video-based point cloud compression.

Segmenting neural network models into priority-based access units balances transmission bandwidth against reconstruction accuracy.

Converting lidar point clouds to video signals leverages mature H.265 compression to resolve low efficiency in traditional encoding methods.

Segmenting volumetric bitstreams into spatial tracks reduces bandwidth waste by decoding only visible 3D regions.

Dilating pixel blobs enables fast symbol matching while NxN cross checks eliminate erroneous matches caused by insufficient compression precision.

Overlaying decoding templates onto encoded patterns in the eye fundus reveals hidden information directly, bypassing remote processing requirements.

A storage control device generates and stores separate image data blocks to isolate object outlines from captured images.

A stop-code tolerant recurrent convolutional neural network generates binary codes for image tiles using adaptive masking logic.

Encoder stores pixel group check values instead of full frames, eliminating expensive frame buffer chips while maintaining video compression capability.

A video playback tool uses graphics primitives to represent texture values for GPU processing.

Segmenting image blocks into pre-processed intermediate representations reduces computational resource consumption during PVRTC texture compression.

A point cloud transmission device encodes data using video-based compression techniques for efficient delivery.

Segmenting point cloud attributes into diffuse, specular, and absorption components reduces data volume while maintaining rendering quality.

Nests compressed BREP data within LOD meshes to reduce file size while preserving geometric accuracy.

Dynamic stage-specific parameters resolve latency and quality trade-offs, reducing compression artifacts during streaming.

A mesh coding method selects between across-parallelogram and reflection prediction for vertex positions.

Adjusts k and g weights in a reward function to control resolution and accuracy during point cloud reduction.

Hierarchical breakpoint coding organizes geometry discontinuities to enable embedded resolution and quality scalability in spatial data compression.

A method compresses dynamic 3D model sequences by storing a reference model and determining fusion parameters through iterative optimization.

An image encoding apparatus generates approximation data for a fourth color using primary color inputs to create difference data for efficient processing.

Conditional entropy estimation replaces empirical thresholds to optimize dictionary construction, reducing file sizes by 10% to 35%.

Implicit neural representations compress video data via shared backbone networks, reducing memory overhead while maintaining high quality at lower bitrates.

Random projection transforms video data into sparse vectors, hiding original information while preserving relational properties for secure machine learning.

Segmenting ARGB assets into lossy RGB and lossless alpha channels improves compression ratios by 4.1x over PNG while preserving transparency quality.

A transmission apparatus segments image data into multiple hierarchy sets to generate separate video streams with regular decoding intervals.

An up-to-end codeword signals remaining block positions in palette mode encoding to minimize bit usage.

Segmenting point clouds into groups enables group-level attribute prediction, resolving high processing time caused by individual point analysis.

Position-based weighting of image elements represents gradual color transitions accurately, reducing memory bandwidth and processing power requirements.

Hierarchical block segmentation improves LiDAR coding efficiency by resolving the trade-off between high throughput and increased processing complexity.

A selective decoding mechanism processes reference frames for non-viewed video sections to manage data flow in virtual reality environments.

Splitting octree data into reliable high-level and non-reliable low-level streams reduces latency while maintaining sufficient decoding accuracy.

Encoding system splits input images into patches or applies principal component analysis to fit low-power AI integrated circuits.

A decoding apparatus selects binary or quadtree splitting modes based on block geometry to optimize data representation.

Adaptive binary arithmetic encoding with look-up tables reduces storage costs for large point clouds while maintaining data completeness.

Segmenting point cloud data into patches reduces transmission latency and encoding complexity while maintaining service quality.

Segmenting neural network topology into specific syntax elements preserves coding accuracy without overwhelming the bitstream with excessive data volume.

Apparent angle context selection reduces computational cost in sparse point cloud compression while maintaining high entropy coding accuracy.

A transformation system reuses cached shadow bitmaps to apply non-linear window effects without regenerating graphics data.

Assigning occupancy map pixel values to indicate stored depth data or fixed-length codewords for dynamic point clouds.

Applying filtering coefficients to K nearest neighbors reduces reconstruction distortion while managing bitstream size overhead.

A verification method tests overdrive compression algorithms by simulating dynamic display sequences to ensure image quality standards.

Hierarchical 3D point encoding inserts identification metadata into the bitstream to distinguish low delay from high efficiency coding methods.

A depth-based modeling method divides uniform sampling grids into sub-grids to generate triangle meshes from depth and color image data.

Adjusting quantization coefficients to enhance display quality of user-selected image areas while reducing data transmission load in non-selected regions.

Adaptive sign allocation reduces code amount for surface-distributed points by ignoring empty regions, resolving compression inefficiency.

A phase shift means generates fractional pixel precision values from adjacent pixels to improve intra prediction accuracy.