Sub-band Scanning Orders for Video Entropy Coding Efficiency
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Solution Overview
Problem
Current video coding techniques face limitations in achieving efficient compression, particularly in entropy coding after sub-band coding, as they do not effectively group high and low energy coefficients for optimal entropy encoding.
Innovation Solution
The proposed solution involves using different scanning orders for various sub-bands (LL, LH, HL, HH) to convert two-dimensional blocks into one-dimensional vectors, allowing for efficient entropy coding by grouping high energy coefficients together and low energy coefficients separately, which can be fixed or adaptive based on statistical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scanning techniques are used for entropy coding of sub-bands, then the coding process is simple, but compression efficiency is limited
Solution Approach 1:
The patent divides the sub-band coding process into multiple scanning stages with different scanning orders (e.g., first scanning order, second scanning order, third scanning order) for different coefficient groups. This segmentation allows high energy coefficients to be grouped separately from low energy coefficients, improving entropy coding efficiency without requiring a complete redesign of the scanning mechanism.
Solution Approach 2:
The patent introduces adaptive scanning that dynamically selects scanning orders based on statistical data and coefficient characteristics. The scanning process transitions from static traditional methods to dynamic adaptive methods, where the scanning order is adjusted based on the energy distribution of coefficients, thereby improving compression efficiency while managing complexity through intelligent adaptation.
2Manufacturing precision
If sub-band coding is applied to video data, then video quality can be enhanced, but entropy coding efficiency decreases without proper scanning
Solution Approach 1:
The patent applies different scanning orders to different regions or groups of coefficients within sub-bands. High energy coefficients are scanned and grouped using one order, while low energy coefficients use another order. This local differentiation ensures that the entropy coding process is optimized for the specific characteristics of each coefficient group, maintaining video quality while improving coding efficiency.
Solution Approach 2:
The patent changes the scanning order parameter based on the energy characteristics of coefficients. By adapting the scanning parameter (order) to match the local energy distribution, the system optimizes the entropy coding process for each region, thereby resolving the contradiction between maintaining video quality and improving coding efficiency.
3Productivity
If multiple scanning orders are used for different sub-bands, then compression efficiency improves, but implementation complexity increases
Solution Approach 1:
The patent performs preliminary analysis of coefficient statistical data before entropy coding to determine the appropriate scanning order for each sub-band or coefficient group. This preliminary action allows the system to pre-determine the optimal scanning strategy, avoiding complex real-time decisions during the actual scanning and coding process, thereby managing implementation complexity while maintaining compression efficiency.
Solution Approach 2:
The system uses the statistical characteristics of the coefficient data itself to determine the scanning order, rather than requiring external complex control mechanisms. The data's own properties guide the scanning process, allowing the system to self-optimize without adding significant external complexity, thus improving compression efficiency while keeping implementation manageable.
Data Source
AI summary
This disclosure describes techniques useful in the encoding and/or decoding of video data of a video sequence. In general, this disclosure sets forth scanning techniques useful in the context of sub-band coding, which may improve the level of compression that can be achieved by entropy coding following sub-band coding. In one example, a method of encoding video data of a video sequence comprises sub-band encoding the video data to generate a plurality of sub-bands, scanning each of the sub-bands from two-dimensional blocks into one-dimensional vectors based on scan orders defined for each of the sub-bands, and entropy encoding each of the scanned sub-bands.