Video Prediction Mode Selection Using Two-Stage Cost Functions
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Solution Overview
Problem
Current video compression standards face inefficiencies in selecting the optimal prediction mode for video encoding, leading to increased computation time and complexity due to the use of rate-distortion cost functions for all available prediction modes.
Innovation Solution
A method and apparatus that select first prediction modes using a sum of absolute transformed difference (SATD) cost function and then choose a final prediction mode using a sum of squared difference (SSD) cost function, reducing computation time by limiting the number of modes considered.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rate-distortion cost functions are used for all available prediction modes, then the optimal prediction mode can be selected, but computation time and complexity increase
Solution Approach 1:
The patent segments the prediction mode selection process into two distinct stages: a first stage that evaluates all n prediction modes using a SATD-based first cost function to identify k candidate modes, and a second stage that evaluates only the k candidate modes using an SSD-based second cost function to select the final prediction mode. This segmentation reduces the computational burden from evaluating all modes with complex rate-distortion functions to evaluating only k modes (where k << n) with simpler functions, thereby reducing computation time while maintaining selection accuracy.
Solution Approach 2:
The patent performs preliminary action by using the first cost function with SATD operation to pre-screen and identify k candidate prediction modes from n total modes before applying the second cost function. This preliminary filtering step eliminates modes that are unlikely to be optimal, so that the computationally more intensive second cost function is applied only to a reduced set of promising candidates, thus reducing overall computation time while preserving the ability to select the optimal mode.
2Reliability
If rate-distortion cost functions are used for all available prediction modes, then the optimal prediction mode can be selected, but computational complexity increases
Solution Approach 1:
The patent segments the computational workload into two phases: Phase 1 computes a first cost function using SATD operation for all n prediction modes to identify k candidates, and Phase 2 computes a second cost function using SSD operation for only the k candidate modes. This segmentation reduces computational complexity because SATD is less complex than full rate-distortion functions, and the second phase is applied to a reduced set of modes, thereby lowering overall computational complexity while maintaining selection accuracy.
Solution Approach 2:
The patent performs preliminary computational action by using the first cost function with SATD to pre-identify k candidate prediction modes before applying the second cost function. This preliminary action reduces the number of modes that require complex SSD and rate-distortion calculations, thereby reducing overall computational complexity while ensuring that the optimal mode is still identified through the second-stage evaluation.
3Measurement precision
If all n prediction modes are evaluated using complex cost functions, then the most accurate prediction mode can be selected, but the number of operations increases
Solution Approach 1:
The patent segments the mode evaluation process into two stages: Stage 1 uses a first cost function with SATD operation to evaluate all n prediction modes and identify k candidates, and Stage 2 uses a second cost function with SSD operation to evaluate only the k candidates. This segmentation reduces the total number of expensive SSD operations from n to k, thereby improving encoding speed (productivity) while maintaining measurement precision through the two-stage evaluation that ensures the optimal mode is not missed.
Solution Approach 2:
The patent performs preliminary evaluation using the first cost function with SATD operation to identify k candidate prediction modes that are most likely to be optimal. This preliminary action reduces the number of modes that require the more computationally intensive second cost function evaluation, thereby increasing encoding speed while preserving measurement precision because the second stage still thoroughly evaluates the promising candidates to ensure accurate mode selection.
Data Source
AI summary
A method and an apparatus for deciding a video prediction mode are provided. The method includes providing n prediction modes, where n is a positive integer, selecting first prediction modes among the n prediction modes using a first cost function, and selecting a final prediction mode among the first prediction modes using a second cost function different from the first cost function. The first cost function includes a sum of absolute transformed difference (SATD) operation, and the second cost function includes a sum of squared difference (SSD) operation.