Luminance Deblocking Filter Control for HDR Block Distortion
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Solution Overview
Problem
Existing deblocking filtering methods struggle to effectively reduce block distortion in high dynamic range (HDR) signals and standard dynamic range (SDR) signals with high light levels, leading to increased noise and reduced compression efficiency.
Innovation Solution
Adaptive deblocking filtering that adjusts filtering strength based on luminance signal levels by comparing the signal levels to threshold values and controlling filtering strength accordingly, allowing for reduced block distortion without compromising compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a fixed threshold setting is used for deblocking filtering, then the device complexity is reduced, but the block distortion is not effectively reduced in HDR signals and SDR signals at high light levels
Solution Approach 1:
The patent implements dynamic threshold adjustment by comparing luminance signal levels to reference values and selecting from multiple predetermined threshold groups. The threshold value changes dynamically based on the luminance level range, transitioning from fixed to adaptive control. This resolves the contradiction by making the filtering parameter dynamic rather than static, improving block distortion reduction while maintaining manageable complexity through predefined threshold groups.
Solution Approach 2:
The patent changes the threshold parameter based on luminance signal level conditions. Multiple predetermined threshold groups are prepared in advance, and the appropriate group is selected according to the current luminance range. This parameter change strategy improves filtering effectiveness across different light levels without requiring complex real-time calculations, thus resolving the contradiction between precision and complexity.
2Productivity
If filtering strength is reduced with smaller quantization parameters to maintain compression efficiency, then compression efficiency is maintained, but block distortion increases at high light levels
Solution Approach 1:
The patent applies different filtering strengths to different luminance regions. By dividing the luminance range into multiple levels and assigning different threshold groups to each level, the system applies stronger filtering where needed (high light levels) and maintains compression efficiency elsewhere. This local differentiation resolves the contradiction by making filtering strength spatially adaptive rather than uniformly reduced.
Solution Approach 2:
The patent changes the threshold parameter based on quantization parameter values and luminance levels. When quantization parameters indicate higher compression needs, the system selects threshold groups appropriate for that compression level, but compensates by applying stronger filtering in high-luminance regions. This dynamic parameter adjustment maintains compression efficiency while preventing block distortion in critical regions.
3Manufacturing precision
If adaptive threshold adjustment based on luminance signal levels is implemented, then block distortion is reduced across varying light levels, but the device complexity increases
Solution Approach 1:
The patent performs preliminary organization of multiple predetermined threshold groups before actual filtering operations. The threshold groups are prepared in advance and stored, so that during filtering, the system only needs to compare luminance levels and select from pre-organized groups rather than calculating thresholds in real-time. This preliminary preparation reduces the complexity of adaptive control while maintaining its effectiveness.
Solution Approach 2:
The patent implements a dynamic yet structured threshold selection mechanism. The system dynamically adapts to different luminance levels and quantization parameters, but the adaptation follows a structured framework with predetermined threshold groups. This structured dynamics approach maintains effectiveness across varying conditions while controlling complexity through organization and pre-computation.
4Device complexity
If fixed filtering strength is applied across all luminance levels, then the device complexity is minimized, but compression efficiency deteriorates due to noticeable block noise in HDR and SDR signals
Solution Approach 1:
The patent segments the luminance signal level range into multiple levels and creates corresponding threshold groups for each segment. Instead of applying a single fixed threshold, the system divides the luminance range and applies appropriate thresholds to each segment. This segmentation improves compression efficiency by adapting to different luminance characteristics while keeping each segment's control relatively simple.
Solution Approach 2:
The patent changes filtering parameters (threshold values) based on luminance signal level conditions. By preparing multiple threshold groups corresponding to different luminance ranges and selecting the appropriate group based on current signal levels, the system achieves adaptive filtering that improves compression efficiency without requiring complex real-time parameter optimization.
Data Source
AI summary
An encoding device 1 includes: a transformer 13 configured to calculate an orthogonal transform coefficient by performing an orthogonal transformation process on a residual image indicating a difference between the input image and a predicted image of the input image; a quantizer 14 configured to generate quantization coefficient by quantizing the orthogonal transform coefficient based on a quantization parameter; an entropy encoder 24 configured to generate encoded data by encoding the quantization coefficient; an image decoder 10 configured to restore an orthogonal transform coefficient from the quantization coefficient based on the quantization parameter and generate a pre-filtering image by adding the predicted image to a residual image restored by performing inverse orthogonal transformation on the orthogonal transform coefficient; and a deblocking filter 18 configured to perform a filtering process on the pre-filtering image and control a filtering strength depending on a result of comparison between a luminance signal level of the pre-filtering image and a luminance threshold value.