Data Period Selection for Image Compression Efficiency
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Solution Overview
Problem
Conventional data coding methods for image processing devices, such as printers, face inefficiencies in compressing data due to limited dictionary size, leading to decreased compression speed and effectiveness, especially when handling mixed content like photographs, characters, and graphics, as they struggle to determine optimal periods for coding.
Innovation Solution
A data processing device and method that selects a data period by counting matches between divided data segments, choosing the period with the highest match count or the longest period when counts are equal, to optimize coding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the dictionary depth is increased to improve compression ratio, then more words can be registered in the dictionary, but the match becomes harder to occur and processing time increases
Solution Approach 1:
The patent divides the data into multiple periods and performs match counting for each period separately. This segmentation allows the system to efficiently search through different periodic patterns without requiring a single large dictionary, thus maintaining fast processing while achieving good compression ratios by selecting the best matching period.
Solution Approach 2:
The patent dynamically selects the optimal period based on match counting results. Instead of using a fixed dictionary depth, the system adjusts the period selection adaptively to match the characteristics of the input data, optimizing both compression ratio and processing speed for different data types.
2Productivity
If a small dictionary is used to reduce processing complexity, then processing speed increases, but the compression ratio decreases
Solution Approach 1:
The patent segments the search space into multiple periods, allowing a small dictionary to effectively cover different periodic patterns. By dividing the data into periods and searching within each period, the system achieves both fast processing (using small dictionary) and good compression (by finding optimal period matches).
Solution Approach 2:
The patent changes the parameter of period selection to optimize compression. By varying the period parameter and selecting the one with the highest match count, the system adapts to different data characteristics, maintaining high processing speed while achieving better compression ratios.
3Loss of information
If period detection is performed in units of one line for dithered image data, then compression can be optimized for periodic patterns, but white images with no periodicity cannot be properly handled
Solution Approach 1:
The patent dynamically adapts to the periodicity characteristics of the input data. By performing match counting for multiple periods and selecting the period with the highest match count, the system can handle both periodic data (like dithered images) and non-periodic data (like white images) optimally, adjusting its behavior based on the actual data characteristics.
Solution Approach 2:
The patent uses match counting as feedback to determine the optimal period. By counting matches for different periods and selecting the one with the highest count, the system receives feedback about the data's periodicity characteristics and adjusts its compression approach accordingly, making it versatile for different image types.
4Adaptability or versatility
If the dictionary size is increased to handle mixed content (photographs, characters, graphics), then more patterns can be matched, but determining the optimal period becomes more difficult
Solution Approach 1:
The patent segments the period determination process into multiple discrete periods to be evaluated. Instead of trying to determine a single optimal period for complex mixed content, the system divides the search into multiple period candidates and evaluates each separately through match counting, simplifying the determination process while maintaining versatility.
Solution Approach 2:
The patent dynamically selects the optimal period based on match counting results for mixed content. By evaluating multiple period candidates and selecting the one with the highest match count, the system adapts to the specific characteristics of mixed content (photographs, characters, graphics) without requiring complex predetermined rules.
Data Source
AI summary
A data processing device includes: a data reading unit that reads a predetermined amount of data; a data-period selection unit that selects a data period in units of the predetermined amount of data; a data clipping unit that clips data from the read data with the data period; an intermediate-data conversion unit that converts the clipped data into intermediate data; and a coding processing unit that codes the intermediate data. The data-period selection unit has a match count unit that obtains count values of matches in data between pieces of divided data that are obtained by dividing the read data in units of respective predetermined periods, and selects one of the periods of divided data that provides largest one of the count values of matches or selects, when some of the count values are the same, largest one of the periods that provide the same count value.