  Image retargeting aims at altering an image size while preserving important
content and minimizing noticeable distortions. However, previous image
retargeting methods create outputs that suffer from artifacts and distortions.
Besides, most previous works attempt to retarget the background and foreground
of the input image simultaneously. Simultaneous resizing of the foreground and
background causes changes in the aspect ratios of the objects. The change in
the aspect ratio is specifically not desirable for human objects. We propose a
retargeting method that overcomes these problems. The proposed approach
consists of the following steps. Firstly, an inpainting method uses the input
image and the binary mask of foreground objects to produce a background image
without any foreground objects. Secondly, the seam carving method resizes the
background image to the target size. Then, a super-resolution method increases
the input image quality, and we then extract the foreground objects. Finally,
the retargeted background and the extracted super-resolued objects are fed into
a particle swarm optimization algorithm (PSO). The PSO algorithm uses aesthetic
quality assessment as its objective function to identify the best location and
size for the objects to be placed in the background. We used image quality
assessment and aesthetic quality assessment measures to show our superior
results compared to popular image retargeting techniques.
