Variable Block Size Encoder for Lossless Image Compression
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Solution Overview
Problem
Conventional image and video encoders fail to efficiently compress data while maintaining image quality, especially when dealing with high-definition and high dynamic range content, leading to increased data sizes and inefficiencies in communication and storage.
Innovation Solution
The method involves subdividing input data into blocks of variable sizes based on content, applying transformations, and dynamically adjusting quality criteria to ensure lossless compression, using techniques like DCT, PCM, and Huffman-coding, and adding header information for decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional codecs employ fixed block sizes for encoding, then the encoding process is simple, but the encoding efficiency deteriorates when dealing with images having extensive areas of constant parameters mixed with highly spatial detailed areas
Solution Approach 1:
The patent applies segmentation by dividing the image into multiple blocks of different sizes (e.g., 8x8, 16x16, 32x32 pixels) based on the content characteristics of each region. Areas with constant parameters are encoded with larger blocks for efficiency, while areas with high spatial detail use smaller blocks for quality preservation, resolving the contradiction between encoding efficiency and complexity.
Solution Approach 2:
The patent implements local quality by assigning different block sizes to different regions of the image according to their specific characteristics. Regions with homogeneous content receive larger block sizes for efficient compression, while regions with complex spatial variations receive smaller block sizes to maintain quality, thereby optimizing the balance between productivity and complexity locally.
2Manufacturing precision
If smaller blocks are used for encoding to maintain image quality, then the encoding quality improves, but the data compression ratio deteriorates
Solution Approach 1:
The patent segments the image into variable-sized blocks, applying smaller blocks only where necessary for quality preservation (in high-detail regions) while using larger blocks in constant regions for maximum compression. This segmented approach resolves the contradiction by not uniformly applying small blocks across the entire image.
Solution Approach 2:
The patent applies local quality by selectively using smaller block sizes only in regions where spatial detail is critical for image quality, while using larger blocks in regions where compression is prioritized. This localized strategy maintains image quality where needed without unnecessarily increasing overall data size.
3Manufacturing precision
If higher encoded image quality is achieved through smaller blocks, then the visual quality improves, but the data compression capability deteriorates
Solution Approach 1:
The patent segments the encoding process into multiple blocks of varying sizes, applying compression strategies that balance quality and compression ratios. By segmenting, the system can achieve high quality in critical regions while maintaining strong compression in less critical regions, resolving the contradiction between quality and compression productivity.
Solution Approach 2:
The patent implements local quality by differentiating encoding strategies across different image regions. High-quality encoding with smaller blocks is applied locally where visual fidelity is most important, while more aggressive compression with larger blocks is applied where it can be tolerated, thereby achieving both high overall quality and good compression productivity.
Data Source
AI summary
An encoder is operable to encode input data to generate corresponding encoded output data. The encoder includes data processing hardware. The encoder compresses content associated with blocks or packets, so that the encoded output data is smaller in size than the input data.