| Author: | Jiang, Shumin |
| Title: | Optimizing JPEG decoder for bitstream-corrupted image restoration |
| Advisors: | Wang, Yi (EEE) |
| Degree: | M.Sc. |
| Year: | 2024 |
| Department: | Department of Electrical and Electronic Engineering |
| Pages: | 50 pages : color illustrations |
| Language: | English |
| Abstract: | With the widespread use of the internet and the rapid advancement of 5G communication technology, a vast amount of information is transmitted over the network in the form of images, making images an important carrier of information. However, during the transmission process, factors such as transmission channel errors, network attacks, data packet loss, and physical damage to storage media inevitably cause interference or damage to the images. This results in random bitstream corruption, leading to image distortion, color deviation, mosaic blurring, and other issues that affect image quality. Image restoration is a hot topic in image processing. However, most existing methods focus on two-dimensional images in the pixel domain, using traditional image processing algorithms such as feature matching globally or locally for image restoration. Few approaches address the repair of undecodable images at the bitstream level. JPEG is an image storage format that offers efficient compression for still images. It can significantly reduce the storage required for an image with minimal loss of resolution and is widely used in various image transmission processes. For corrupted JPEG images, traditional decoders may lose some information after encountering errors, leading to incorrect decoding of subsequent bits or error propagation during decoding. Therefore, this paper proposes a new algorithm to achieve higher image quality. The specific content is as follows: Based on the introduction of the JPEG standard and design process, this paper conducts an in-depth study of the JPEG decoder. Compressed Sensing allows for random undersampling of sparse signals at a rate significantly lower than the Nyquist rate, enabling the reconstruction of the original signal through sparse recovery techniques. According to the characteristics of compressed sensing, it is applied to the DCT coefficients in the minimum coding unit. By introducing different types of noise to artificially corrupt the bitstream, a unique dataset was created, extending its applicability by including grayscale images. Images with corrupted bitstreams showed improved visual quality after passing through this decoder. Additionally, the block alignment algorithm was created for DC error propagation, enhancing the visual effect of the images. The values of PSNR and SSIM are improved under different error rate conditions. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8312.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.7 MB | Adobe PDF | View/Open |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/13904