Binary Codeword Determination for Transform Coefficients
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Solution Overview
Problem
Current video compression technologies, particularly High Efficiency Video Coding (HEVC), face challenges in efficiently encoding and decoding large input images and macroblocks due to high video data density, requiring improved methods for coding transform coefficients within transform units.
Innovation Solution
A method for determining binary codewords for transform coefficients in an efficient manner by using a processor to decode encoded frames, identifying parameter variables, and converting binary codewords into symbols based on a parameter variable associated with a maximum symbol value that denotes a truncation point, allowing for accurate reconstruction of frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video compression methods are used for HEVC, then video data can be compressed and transmitted, but the encoding and decoding of large input images and macroblocks becomes inefficient due to high video data density
Solution Approach 1:
The patent divides transform units into sub-blocks and processes transform coefficients in a segmented manner. Each sub-block is handled independently with its own parameter variable, allowing parallel processing and reducing the computational burden on large macroblocks. This segmentation enables efficient handling of high video data density by breaking down complex encoding/decoding tasks into smaller, manageable units.
2Measurement precision
If transform coefficients are coded with high precision to maintain video quality, then reconstruction accuracy improves, but the complexity of determining binary codewords increases
Solution Approach 1:
The patent introduces a dynamic parameter variable that is updated based on the absolute level values of transform coefficients. This parameter variable adapts to the local characteristics of each sub-block, allowing the coding precision to be optimized dynamically. The parameter variable controls the truncation point in binary codeword generation, enabling high precision where needed while reducing complexity where transform coefficient values are small or zero.
3Ease of manufacture
If a fixed truncation point is used for binary codeword generation, then the coding process is simple, but accuracy is lost when transform coefficient values exceed the truncation point
Solution Approach 1:
The patent changes the truncation point parameter dynamically based on the actual transform coefficient values in each sub-block. The parameter variable is updated to reflect the maximum absolute level value encountered, ensuring that the truncation point is always sufficient to represent all transform coefficients in the current sub-block. This resolves the contradiction by making the coding process adaptive rather than fixed, maintaining simplicity while eliminating precision loss.
Data Source
AI summary
Generating a reconstructed may include reading a binary codeword corresponding to a transform coefficient for a sub-block of a transform unit, the transform coefficient having a quantized value, identifying a value of a parameter variable as zero in response to a determination that the transform coefficient is a first transform coefficient for the sub-block, and otherwise as an updated parameter variable value, converting the binary codeword into a symbol based on the value of the parameter variable, determining the absolute value of the transform coefficient corresponding to the symbol, wherein the quantized value for the transform coefficient is equal to or greater than a threshold value, by adding the threshold value to the symbol, including the transform coefficient in the sub-block of the transform unit, and generating a portion of the reconstructed frame based on the transform unit.