Decompression of JPEG Document Images: A Survey Paper

JPEG Decompression techniques are very useful in 3G/4G based markets, handheld devices and infrastructures. There are many challenging issues in previously proposed decompression methods, like very high computational cost, and heavy distortion in ringing and blocking artifacts which makes the image invisible. To improve the visual quality of the JPEG document images at low bit rate and at low computational cost, we are going to implement the decompression technique for JPEG document images. We first divide the JPEG document image into smooth and non-smooth blocks with the help of Discrete Cosine Transform (DCT). Then the smooth blocks (background , uniform region) are decoded in the transform domain by minimizing the Total Block Boundary Variation(TBBV). In this we propose to compute the block variation directly in the DCT domain at the super pixel level. The super pixel have size n*n, each super pixel is assigned with an average intensity value. The smooth blocks are then reconstructed by using the Newton’s method. The implementation of the smooth block decompression will be done here. The non-smooth blocks of the document image contains the text and graphics/line drawing objects. The post processing algorithm will be introduced which takes into consideration the specificities of document content. The inverse DCT is applied to represent the image in spatial domain. So the implementation of the non-smooth block decompression will be done here. Finally, we design different experimental results and analyze that our system is better than the existing. And it will show the quality improvement of decompressed JPEG document image

A document image model and estimation algorithm for optimized JPEG decompression

The JPEG standard is one of the most prevalent image compression schemes in use today. While JPEG was designed for use with natural images, it is also widely used for the encoding of raster documents. Unfortunately, JPEG\u27s characteristic blocking and ringing artifacts can severely degrade the quality of text and graphics in complex documents. We propose a JPEG decompression algorithm which is designed to produce substantially higher quality images from the same standard JPEG encodings. The method works by incorporating a document image model into the decoding process which accounts for the wide variety of content in modern complex color documents. The method works by first segmenting the JPEG encoded document into regions corresponding to background, text, and picture content. The regions corresponding to text and background are then decoded using maximum a posteriori (MAP) estimation. Most importantly, the MAP reconstruction of the text regions uses a model which accounts for the spatial characteristics of text and graphics. Our experimental comparisons to the baseline JPEG decoding as well as to three other decoding schemes, demonstrate that our method substantially improves the quality of decoded images, both visually and as measured by PSNR

Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration

Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual quality. For these reasons, quality enhancement of compressed images has become a popular research topic. While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks. In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data. We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the "AIM 2022 Challenge on Super-Resolution of Compressed Image and Video".Comment: European Conference on Computer Vision (ECCV 2022) Workshop