Video Decoding Precision Control Across GOP Reference Frames
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Solution Overview
Problem
Existing video decoders face challenges in efficiently managing power consumption while decoding video images, particularly when dealing with different types of reference frames in a group of pictures (GOP), leading to inefficient energy use and potential overheating.
Innovation Solution
An image processing method that determines image reference and non-reference frames within a GOP, applying different decoding precisions based on power-consumption constraints, including zeroing residual coefficients and inverse transforming blocks with varying precision levels to optimize energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform high precision decoding is applied to all reference frames, then image quality is maintained, but power consumption increases
Solution Approach 1:
The patent applies different decoding precisions to different regions (frame types) within the GOP structure. Image reference frames (I-frames and P-frames) receive high precision decoding while non-image reference frames (B-frames) receive low precision decoding. This local differentiation maintains image quality where needed while reducing power consumption in less critical areas.
Solution Approach 2:
The patent segments the GOP structure into distinct categories: image reference frames and non-image reference frames. By dividing the decoding process into these segments with different precision levels, the system optimizes the balance between image quality and power consumption across the entire video stream.
2Measurement precision
If high precision decoding is applied to all frames, then image quality is maintained, but device complexity increases
Solution Approach 1:
The patent implements local quality by applying high decoding precision only to image reference frames and low decoding precision to non-image reference frames. This selective approach reduces the overall computational complexity of the decoding device while maintaining image quality in critical frames.
Solution Approach 2:
By segmenting the decoding process into two distinct pathways (high precision for image reference frames, low precision for non-image reference frames), the patent reduces the average computational load on the device, thereby lowering overall device complexity.
3Use of energy by moving object
If low precision decoding is applied to reduce power consumption, then energy usage decreases, but image quality deteriorates
Solution Approach 1:
The patent applies low precision decoding only to non-image reference frames (B-frames) where image quality requirements are lower, while maintaining high precision for image reference frames (I-frames and P-frames). This local differentiation reduces overall power consumption while preserving image quality in critical frames.
Solution Approach 2:
By segmenting the GOP into image reference frames and non-image reference frames with different decoding precision levels, the patent achieves a balance between power consumption and image quality, applying low precision only where it does not significantly impact overall image quality.
Data Source
AI summary
An image processing method is provided. The method includes: obtaining a video bitstream; determining an image reference frame according to a structure of a group of pictures (GOP) of the video bitstream; determining a first decoding precision and a second decoding precision according to a power-consumption constraint instruction; and decoding a to-be-decoded block of the image reference frame of the GOP according to the first decoding precision, and decoding a to-be-decoded block of a non-image reference frame of the GOP according to the second decoding precision.