Image Frame Encoding with Local Bit Depth Reduction
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Solution Overview
Problem
Traditional video compression schemes are limited by the bit depth of the input image frames, leading to inefficient storage requirements and high costs due to the size of encoded video streams.
Innovation Solution
A method involving an image processing device that identifies specific areas in an image frame based on certain criteria, reduces the bit depth by replacing least significant bit values with dummy values, and encodes the frame to reduce redundancy and storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional video compression schemes are used with fixed bit depth, then encoding simplicity is maintained, but storage space consumption increases
Solution Approach 1:
The image frame is divided into multiple blocks, and each block is independently processed with its own bit depth reduction factor. This allows selective application of bit depth reduction to different regions, achieving storage space reduction while maintaining encoding manageability through localized processing.
Solution Approach 2:
Different bit depth reduction factors are applied to different blocks based on their specific characteristics (e.g., complexity, importance). This local differentiation optimizes storage efficiency by applying more aggressive reduction where acceptable while preserving quality where needed, resolving the contradiction between compression and complexity.
2Quantity of substance
If bit depth is reduced by replacing LSB values with dummy values, then encoded video stream size decreases, but image quality deteriorates
Solution Approach 1:
Instead of uniformly reducing bit depth across the entire image, the method applies partial bit depth reduction only to specific blocks where it is acceptable. The bit depth reduction factor is determined based on block characteristics, ensuring that quality is maintained in critical areas while achieving compression in less sensitive areas.
Solution Approach 2:
The bit depth parameter is dynamically adjusted for different blocks based on their characteristics. By changing the bit depth parameter selectively rather than globally, the method achieves video stream size reduction while minimizing quality deterioration through adaptive parameter modification.
3Quantity of substance
If uniform bit depth is applied to all image frames, then processing simplicity is maintained, but storage efficiency is reduced
Solution Approach 1:
The processing complexity is managed by segmenting the image into blocks that can be independently analyzed and processed. Each block's bit depth reduction factor is determined based on its specific characteristics, allowing storage efficiency optimization without requiring complex global processing of the entire frame.
Solution Approach 2:
The bit depth reduction factors for all blocks are determined in advance during the encoding process before actual compression. This preliminary determination of processing parameters simplifies the subsequent encoding operations while achieving storage efficiency gains through pre-planned differential processing.
Data Source
AI summary
A method for encoding an image frame, performed by an image processing device, comprising obtaining image data, and identifying an image area in an image frame based on that the image area fulfilling an identification criterion. The method further comprises determining a bit depth reduction factor for the identified image area by analyzing the image data in the identified image area, and replacing some of the bit values of the pixel values in the identified image area with dummy values. How many of the bit values that are replaced with dummy values is defined by the bit depth reduction factor. The method comprises encoding the image frame upon said some of the bit values having been replaced in the identified image area.