Point Cloud Transform Coefficient Partition Coding for Sparse Prediction

The patent uses frequency-sorted look-up tables and caches to store and reuse previously computed transform coefficients and their coded representations. When neighboring samples are insufficient, the system copies and reuses existing coefficient patterns from the look-up table, maintaining compression efficiency without requiring additional neighboring samples for prediction.

The patent pre-computes and stores transform coefficients in frequency-sorted look-up tables before actual encoding. This preliminary action allows the system to quickly retrieve and reuse pre-coded coefficient patterns during compression, eliminating the need for complex real-time predictions when neighboring samples are limited.

The patent extracts frequently occurring transform coefficient patterns and stores them in frequency-sorted look-up tables. During encoding, these pre-extracted patterns are directly retrieved and reused, eliminating the need to apply complex entropy coding algorithms to every coefficient individually, thus reducing computational complexity while maintaining coding completeness.

The patent changes the coding parameter from full entropy coding of each coefficient to selective reuse of pre-coded patterns from look-up tables. This parameter change in the coding strategy reduces computational complexity by avoiding redundant encoding operations while preserving the completeness of coefficient representation through the cached patterns.