Adaptive Block Decoder for Efficient Variable-Size Data Decompression
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Solution Overview
Problem
Conventional codecs are inefficient in compressing images and videos, especially when dealing with extensive areas of constant parameters and highly spatially detailed areas, leading to increased data size and loss of quality, and fail to address the growing demand for efficient compression of large datasets such as genomic data and IoT applications.
Innovation Solution
A decoder method that processes encoded input data by extracting header information, creating blocks, splitting or combining them based on transformation information, and applying inverse transformations to decode data without substantial loss of quality, supporting various data types including images, videos, audio, and genomic data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional codecs use fixed block sizes for encoding, then the encoding process is simple, but encoding efficiency decreases for images with varying spatial detail
Solution Approach 1:
The image is divided into multiple blocks of different sizes (e.g., 16x16, 8x8, 4x4 pixels) rather than using a fixed block size. This segmentation allows the encoder to adapt to local image characteristics, applying appropriate encoding strategies to each block size, thereby improving overall encoding efficiency while maintaining processability.
Solution Approach 2:
The block size is made dynamic and adaptive rather than fixed. The encoder selectively varies block sizes based on local image content characteristics, such as spatial detail and texture complexity. This dynamic approach allows efficient compression of both smooth areas (using larger blocks) and detailed areas (using smaller blocks), resolving the contradiction between simplicity and efficiency.
2Manufacturing precision
If smaller block sizes are used for encoding, then encoded image quality improves, but data compression ratio decreases
Solution Approach 1:
Different block sizes are applied to different regions of the image based on local characteristics. Smooth regions use larger blocks for better compression, while detailed regions use smaller blocks for higher quality. This local adaptation resolves the contradiction by optimizing both quality and compression ratio in their respective appropriate regions.
Solution Approach 2:
The block size is dynamically selected based on local image content analysis. The encoder adapts block size to match local spatial detail and texture complexity, ensuring high quality where needed while maintaining efficient compression in other areas, thus balancing quality and data size.
3Productivity
If multiple different block sizes are used for encoding, then encoding efficiency improves, but device complexity increases
Solution Approach 1:
The encoding process is segmented into distinct stages: block size determination, transformation, and decoding. The decoder receives explicit block size information and processes each block according to its specified size, managing complexity through structured segmentation of the encoding/decoding pipeline.
Solution Approach 2:
Block size information is determined and communicated during the encoding phase before decoding. The decoder receives pre-specified block size data along with the encoded image, allowing it to prepare appropriate processing for each block without complex real-time analysis, thus improving efficiency while managing decoder complexity.
4Quantity of substance
If conventional codecs compress data extensively, then storage requirements decrease, but loss of information increases
Solution Approach 1:
The compression approach is dynamically adjusted based on local image characteristics. The encoder selects appropriate block sizes and encoding parameters to achieve efficient compression while preserving important image information, adapting the compression intensity to match local content importance and detail level.
Solution Approach 2:
Different compression strategies are applied to different regions of the image. Critical regions with high spatial detail use smaller blocks and more conservative compression to preserve information, while less critical smooth regions use larger blocks and more aggressive compression, optimizing the balance between storage efficiency and information preservation.
Data Source
AI summary
A decoder includes data processing hardware which is operable to: process encoded input data to extract header information indicative of individual blocks and/or packets, and information indicative of transformations employed to the individual blocks and/or packets; create an initial set of individual blocks and/or packets based on received header data, and prepare a data field in a data storage arrangement for receiving decoded individual block and/or packet content; split or combine individual blocks and/or packets in the data field according to information indicative of splitting/combining; retrieve information describing the transformations and then applying an inverse of the transformations for decoding the encoded and compressed original block and/or packet data to generate corresponding decoded block and/or packet content for populating data field; and when the encoded input data has been at least partially decoded, outputting data from the data field as the decoded output data.