Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Algorithms for Manipulating Compressed Images
IEEE Computer Graphics and Applications
Content-based image enhancement in the compressed domain based on multi-scale α-rooting algorithm
Pattern Recognition Letters
Automatic image enhancement by content dependent exposure correction
EURASIP Journal on Applied Signal Processing
Text detection, localization, and tracking in compressed video
Image Communication
A compressed domain scheme for classifying block edge patterns
IEEE Transactions on Image Processing
Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy
IEEE Transactions on Image Processing
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With the increasing sizes of high resolution images, their storage and processing directly in the compressed domain has significantly gained importance. Algorithms for compressed domain image processing provide a powerful computational alternative to classical (pixel level) based implementations. While linear algorithms can be applied straightforward to the JPEG compressed images, this is not the case for nonlinear image processing, as for example contrast enhancement algorithms. In this paper a new implementation in the compressed domain of a very efficient contrast enhancement, based on fuzzy set modeling and on a fuzzy intensification operator, is presented. The fuzzy set parameters are adaptively chosen by analyzing the statistics of the image data in the compressed domain, in order to optimally enhance the image contrast. The nonlinear enhancement procedure requires a grey level threshold, for which an adaptive implementation, taking into account the frequency content of each coefficient block in the DCT (Discrete Cosine Transform) encoded JPEG image is proposed. This guarantees the optimal quality at minimum computational cost. The experimental results for a set of various contrast images validate the good performance and functionality of the proposed implementation.