Block Map Image Decoding for Adaptive Block Splitting
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Solution Overview
Problem
Existing image encoding and decoding methods struggle to efficiently handle high-resolution or high-quality image content by optimizing data units for compression, leading to suboptimal compression ratios and decoding efficiency.
Innovation Solution
The method employs a block map to determine decoded and non-decoded blocks, allowing for adaptive reference to neighboring regions during decoding and encoding, using a block map to update availability based on decoding status and considering square or non-square shapes of blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image data is compressed using traditional codecs with fixed data unit sizes, then encoding and decoding processes are simple, but compression ratios are suboptimal for high-resolution images
Solution Approach 1:
The image is divided into multiple blocks of different sizes (e.g., 16x16, 32x32, 64x64 pixels) rather than using a fixed data unit size. This segmentation allows the encoder to adaptively select appropriate block sizes for different regions of the image, improving compression efficiency for high-resolution content while managing encoding complexity through standardized block types.
2Productivity
If arbitrary processing methods are used to compress high-resolution images, then compression efficiency improves, but decoding accuracy and reliability deteriorate
Solution Approach 1:
Different blocks within the image are processed with different quality levels and compression parameters based on their local characteristics. Important regions (e.g., faces, text) use higher quality settings while less critical areas use more aggressive compression, maintaining overall decoding accuracy while improving compression efficiency.
Solution Approach 2:
The encoding parameters such as block size, transformation type, and quantization strength are dynamically adjusted based on local image characteristics like variance, edge detection, and frequency content. This adaptive approach maintains decoding accuracy in complex regions while achieving better compression in uniform areas.
3Productivity
If data units are split using various methods to determine optimal size, then compression performance improves, but the complexity of determining data unit size increases
Solution Approach 1:
The system pre-defines a set of standard block sizes (e.g., 16x16, 32x32, 64x64) and transformation types that can be selected based on simple criteria. This preliminary preparation of encoding options avoids complex real-time optimization while still achieving good compression performance through adaptive selection from the predefined set.
Data Source
AI summary
Provided are a method and device for encoding or decoding an image by using a block map. In the method and device for decoding an image according to an embodiment set forth herein, a bitstream of an encoded image is received, one or more blocks in a picture are determined using split information obtained from the bitstream, a block map indicating whether or not a previous block among the one or more blocks has been decoded is determined, a neighboring region which is available to be referred to for a current block to be decoded in a certain scan order among the one or more blocks is determined, and the current block is decoded based on the neighboring region which is available to be referred to.