# Fast 3D-HEVC Depth Map Coding Method Based on Spatio-Temporal Correlation and a Two-Stage Mode Decision Framework

## TL;DR

This paper introduces a fast method for encoding depth maps in 3D-HEVC by using spatio-temporal correlations and a two-stage decision framework to reduce computational time.

## Contribution

A novel two-stage intra-mode decision algorithm for depth maps using naive Bayes and FSVM to improve efficiency and accuracy.

## Key findings

- The proposed method reduces average encoding time by 52.30% with minimal BDBR increase.
- It maintains stable performance across various resolutions and scene types.
- The framework balances encoding complexity and rate-distortion performance effectively.

## Abstract

Efficient intra-mode decision for depth maps assumes a pivotal role in augmenting the overall performance of 3D-HEVC. Existing research endeavors predominantly rely on fast mode screening strategies grounded in texture characteristics or machine learning techniques. These strategies, to a certain extent, mitigate the complexity of mode search. Nevertheless, these approaches often fall short of fully leveraging the intrinsic spatio-temporal correlations within depth maps. Moreover, strategies relying on deterministic classifiers exhibit insufficient discrimination reliability in regions featuring edge mutations or intricate structures. To tackle these challenges, this paper presents a two-stage fast intra-mode decision algorithm for depth maps, integrating naive Bayes probability estimation and fuzzy support vector machine (FSVM). Initially, it confines the candidate mode space through spatio-temporal prior modeling. Subsequently, FSVM is employed to enhance the decision accuracy in regions with low confidence. This methodology constructs a joint mode decision framework spanning from probability screening to refined classification. By doing so, it significantly reduces the computational burden while preserving rate-distortion performance, thereby attaining an effective equilibrium between encoding complexity and performance. Experimental findings demonstrate that the proposed algorithm reduces the average encoding time by 52.30% with merely a 0.68% increment in BDBR. Additionally, it showcases stable universality across test sequences of diverse resolutions and scenes.