Image Processing Apparatus Adaptive Quantization Edge Quality

The patent applies local quality by dividing the image into multiple blocks and determining different quantization parameters for each block based on local characteristics. Edge portions with large pixel value differences are identified and assigned smaller quantization steps to preserve image quality, while flat regions use larger quantization steps for better compression. This localized adaptation resolves the contradiction by maintaining high image quality where needed while achieving efficient compression elsewhere.

The patent implements dynamic quantization parameter selection that adapts to local image characteristics. The system dynamically determines quantization parameters based on the variance or gradient of pixel values in each block, switching between different quantization strategies for different regions. This dynamic adaptation allows the encoding system to optimize both image quality and compression efficiency for each local region, resolving the static limitation of conventional DPCM approaches.

The patent applies local quality by determining different quantization parameters for different blocks based on their local characteristics. Blocks containing sharp edges are identified through gradient or variance analysis and assigned smaller quantization steps to preserve edge quality, while flat regions use larger quantization steps for better compression. This selective approach resolves the contradiction by applying appropriate quantization intensity to each local region rather than using a uniform quantization strategy.

The patent changes the quantization parameter based on local image characteristics such as variance or gradient magnitude. The system dynamically adjusts the quantization step size for each block, using smaller steps for high-variation regions (edges) and larger steps for low-variation regions (flat areas). This parameter adaptation resolves the contradiction by optimizing the trade-off between code length and image quality for each local region.

The patent applies preliminary action by performing a preliminary analysis of each block's characteristics (variance, gradient, or edge detection) before determining the quantization parameter. This preliminary classification allows the system to select appropriate quantization parameters in advance for each block type, optimizing both compression efficiency and image quality without requiring complex real-time adjustments during encoding. The preliminary segmentation into different block types enables efficient subsequent processing.

The patent segments the image into multiple blocks and applies different quantization parameters to different segments based on their local characteristics. By dividing the image and analyzing each segment independently, the system can optimize compression for flat regions while preserving quality in edge regions. This segmentation approach balances encoding complexity with compression efficiency by applying simple rules to each segment rather than complex global optimization.