Video Frame Reordering for Spatio-Temporal Wavelet Compression
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Solution Overview
Problem
Current video encoding techniques are inefficient in reducing coding costs, as they typically filter temporally successive frames, which may not always provide the highest similarity and thus the most effective coding, leading to increased bit usage.
Innovation Solution
Adaptive temporal decomposition method that reorders frames based on computed similarity measures, allowing for non-successive frame filtering, using local and global criteria to determine the optimal frame order for each group of frames, and transmitting reordering information for decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If temporally successive frames are filtered together, then the encoding process is simple and fast, but the similarity between frames may not be high enough leading to increased coding cost
Solution Approach 1:
The patent applies preliminary action by reordering frames before the filtering process. Specifically, frame reordering is performed as a preliminary step to group frames with higher similarity together, so that when filtering occurs subsequently, the maximum similarity benefit is achieved. This separates the reordering operation from the encoding process itself, allowing the filtering stage to work with optimally arranged frames rather than temporally successive ones.
Solution Approach 2:
The patent implements dynamics by making the frame ordering adaptive rather than fixed. The reordering process dynamically arranges frames based on their similarity characteristics, allowing the system to adapt to different video content and motion patterns. This dynamic reordering enables the filtering process to always work with the most similar frame pairs available, rather than being constrained to temporal succession.
2Loss of energy
If frames are reordered to maximize similarity, then coding cost is reduced, but the device complexity increases due to additional reordering operations
Solution Approach 1:
The patent applies segmentation by separating the frame reordering operation from the video encoding process. The reordering is performed as an independent preliminary step that operates on the sequence of frames before they enter the main encoding pipeline. This segmentation allows the complexity of reordering to be isolated and managed separately, while the main encoding process remains unchanged and can leverage the pre-arranged frame sequence for more efficient filtering.
3Measurement precision
If non-successive frames are filtered together, then higher similarity can be achieved, but the temporal decomposition scheme becomes more complex
Solution Approach 1:
The patent uses preliminary action to compute similarity measures between frames before the temporal decomposition process. By calculating and storing similarity metrics in advance, the system can make informed decisions about frame reordering without adding complexity to the actual decomposition scheme. The pre-computed similarity information guides the reordering, enabling non-successive frames to be filtered together while keeping the decomposition logic relatively simple.
Data Source
AI summary
In video encoding, the video frames are spatio-temporally filtered for reduction of spatial and temporal redundancy before they are entropy encoded. Known filtering schemes consider temporally successive frames and are static. It is probable but not necessary that successive frames are most efficient to encode. Therefore, a plurality or all possible frame order permutations are considered for a group of frames (GOP) and evaluated based on a global criterion, which is the sum of local criterion values computed over successive subsets of permuted frames. The local criterion value is deduced from motion estimation processed on each considered set of frames. The best ordering is chosen as the one that minimizes the global criterion value.