TY - GEN
T1 - Memory-efficient contour-based region-of-interest coding of arbitrarily large images
AU - Sadaka, Nabil G.
AU - Abousleman, Glen P.
AU - Karam, Lina
PY - 2007/11/19
Y1 - 2007/11/19
N2 - In this paper, we present a memory-efficient, contour-based, region-of-interest (ROI) algorithm designed for ultra-low-bit-rate compression of very large images. The proposed technique is integrated into a user-interactive wavelet-based image coding system in which multiple ROIs of any shape and size can be selected and coded efficiently. The coding technique compresses region-of-interest and background (non-ROI) information independently by allocating more bits to the selected targets and fewer bits to the background data. This allows the user to transmit large images at very low bandwidths with lossy/lossless ROI coding, while preserving the background content to a certain level for contextual purposes. Extremely large images (e.g., 65000 × 65000 pixels) with multiple large ROIs can be coded with minimal memory usage by using intelligent ROI tiling techniques. The foreground information at the encoder/decoder is independently extracted for each tile without adding extra ROI side information to the bit stream. The arbitrary ROI contour is down-sampled and differential chain coded (DCC) for efficient transmission. ROI wavelet masks for each tile are generated and processed independently to handle any size image and any shape/size of overlapping ROIs. The resulting system dramatically reduces the data storage and transmission bandwidth requirements for large digital images with multiple ROIs.
AB - In this paper, we present a memory-efficient, contour-based, region-of-interest (ROI) algorithm designed for ultra-low-bit-rate compression of very large images. The proposed technique is integrated into a user-interactive wavelet-based image coding system in which multiple ROIs of any shape and size can be selected and coded efficiently. The coding technique compresses region-of-interest and background (non-ROI) information independently by allocating more bits to the selected targets and fewer bits to the background data. This allows the user to transmit large images at very low bandwidths with lossy/lossless ROI coding, while preserving the background content to a certain level for contextual purposes. Extremely large images (e.g., 65000 × 65000 pixels) with multiple large ROIs can be coded with minimal memory usage by using intelligent ROI tiling techniques. The foreground information at the encoder/decoder is independently extracted for each tile without adding extra ROI side information to the bit stream. The arbitrary ROI contour is down-sampled and differential chain coded (DCC) for efficient transmission. ROI wavelet masks for each tile are generated and processed independently to handle any size image and any shape/size of overlapping ROIs. The resulting system dramatically reduces the data storage and transmission bandwidth requirements for large digital images with multiple ROIs.
KW - Differential chain coding
KW - Lossy/lossless coding
KW - Region-of-interest coding
KW - Wavelet-based coding
UR - http://www.scopus.com/inward/record.url?scp=36048967892&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36048967892&partnerID=8YFLogxK
U2 - 10.1117/12.719881
DO - 10.1117/12.719881
M3 - Conference contribution
AN - SCOPUS:36048967892
SN - 0819467014
SN - 9780819467010
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Mobile Multimedia/Image Processing for Military and Security Applications 2007
T2 - Mobile Multimedia/Image Processing for Military and Security Applications 2007
Y2 - 11 April 2007 through 12 April 2007
ER -