  The problem of generating textual descriptions for the visual data has gained
research attention in the recent years. In contrast to that the problem of
generating visual data from textual descriptions is still very challenging,
because it requires the combination of both Natural Language Processing (NLP)
and Computer Vision techniques. The existing methods utilize the Generative
Adversarial Networks (GANs) and generate the uncompressed images from textual
description. However, in practice, most of the visual data are processed and
transmitted in the compressed representation. Hence, the proposed work attempts
to generate the visual data directly in the compressed representation form
using Deep Convolutional GANs (DCGANs) to achieve the storage and computational
efficiency. We propose GAN models for compressed image generation from text.
The first model is directly trained with JPEG compressed DCT images (compressed
domain) to generate the compressed images from text descriptions. The second
model is trained with RGB images (pixel domain) to generate JPEG compressed DCT
representation from text descriptions. The proposed models are tested on an
open source benchmark dataset Oxford-102 Flower images using both RGB and JPEG
compressed versions, and accomplished the state-of-the-art performance in the
JPEG compressed domain. The code will be publicly released at GitHub after
acceptance of paper.
