IEEE Trans Image Process - Saliency detection in the compressed domain for adaptive image retargeting.

Tópicos

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Resumo

Saliency detection plays important roles in many image processing applications, such as regions of interest extraction and image resizing. Existing saliency detection models are built in the uncompressed domain. Since most images over Internet are typically stored in the compressed domain such as joint photographic experts group (JPEG), we propose a novel saliency detection model in the compressed domain in this paper. The intensity, color, and texture features of the image are extracted from discrete cosine transform (DCT) coefficients in the JPEG bit-stream. Saliency value of each DCT block is obtained based on the Hausdorff distance calculation and feature map fusion. Based on the proposed saliency detection model, we further design an adaptive image retargeting algorithm in the compressed domain. The proposed image retargeting algorithm utilizes multioperator operation comprised of the block-based seam carving and the image scaling to resize images. A new definition of texture homogeneity is given to determine the amount of removal block-based seams. Thanks to the directly derived accurate saliency information from the compressed domain, the proposed image retargeting algorithm effectively preserves the visually important regions for images, efficiently removes the less crucial regions, and therefore significantly outperforms the relevant state-of-the-art algorithms, as demonstrated with the in-depth analysis in the extensive experiments.

Resumo Limpo

salienc detect play import role mani imag process applic region interest extract imag resiz exist salienc detect model built uncompress domain sinc imag internet typic store compress domain joint photograph expert group jpeg propos novel salienc detect model compress domain paper intens color textur featur imag extract discret cosin transform dct coeffici jpeg bitstream salienc valu dct block obtain base hausdorff distanc calcul featur map fusion base propos salienc detect model design adapt imag retarget algorithm compress domain propos imag retarget algorithm util multioper oper compris blockbas seam carv imag scale resiz imag new definit textur homogen given determin amount remov blockbas seam thank direct deriv accur salienc inform compress domain propos imag retarget algorithm effect preserv visual import region imag effici remov less crucial region therefor signific outperform relev stateoftheart algorithm demonstr indepth analysi extens experi

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