Video enhancement is one of the most important and difficult components in video research. The aim of video enhancement is to improve the visual appearance of the video, or to provide a "better"transform representation for future automated video processing, such as analysis, detection, segmentation, recognition, surveillance, traffic, criminal justice systems. In this paper, we present an overview of video enhancement processing and analysis algorithms used in these applications. The existing techniques of video enhancement can be classified into two categories: Self-enhancement and Context- based fusion enhancement. More specifically, we categorize processing methods based representative techniques of video enhancement. Thus, the contribution of the paper has fourfold: (1) to classify and review video enhancement processing algorithms, (2) to discuss the advantages and disadvantages of these algorithms, (3) according to this integrated consideration, attempt an evaluation of shortcomings and general needs in this field of active research, and (4) we will point out promising directions on research for video enhancement for future research.

/pdf/a-survey-of-video-enhancement-techniques-2ai6epsmx8.pdf

A Survey of Video Enhancement Techniques

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.

Fuzzy intensification operator based contrast enhancement in the compressed domain

Our objective is to investigate image processing directly in the compressed domain, without full decompression. Compressed domain image processing algorithms provide a powerful computational alternative to classical (pixel level) based implementations. The field is just emerging and the algorithms reported in the literature are mostly based on linear arithmetic operations between pixels. In this paper, the problem of implementing a non- linear operator, using compressed domain processing is addressed. A new algorithm for digital image enhancement, using fuzzy theory, adapted to the frequency content of each coefficient block in the DCT (Discrete Cosine Transform) encoded JPEG image is developed and proposed.

Fuzzy contrast enhancement for images in the compressed domain

In the past few years the resolution of images increased and the requirement for large storage space and fast process, directly in the compressed domain, becomes essential. Fuzzy rule-based contrast enhancement, is a well-known rather simple approach with good visual results. As any fuzzy algorithm, it is by default nonlinear, thus not straightforward applicable on the JPEG bitstream data - zig-zag ordered quantized DCT (Discrete Cosine Transform) coefficients. Because of their nonlinear nature the fuzzy techniques don't have yet a well-defined strategy for their implementation in the compressed domain. In this paper, we propose an implementation strategy suitable for single input - single output Takagi-Sugeno fuzzy systems with trapezoidal shaped input membership function, directly in the JPEG compressed domain. The fuzzy sets parameters are adaptively chosen by analyzing the histogram of the image data in the compressed domain, in order to optimally enhance the image contrast. The fuzzy rule-based algorithm requires some threshold comparisons, for which an adaptive implementation, taking into account the frequency content of each block in the compress domain JPEG image is proposed. This guarantees the minimal error implementation at minimum computational cost.

/pdf/computationally-efficient-algorithm-for-fuzzy-rule-based-2tw5gt9emq.pdf

Computationally efficient algorithm for fuzzy rule-based enhancement on JPEG compressed color images

Since the resolution of images increased in the past few years, the requirements for large storage space and fast process, directly in the compressed domain, becomes essential. Fuzzy rule-based contrast enhancement, is a well-known rather simple approach with good visual results, but as any fuzzy algorithm, it is by default nonlinear, thus not straightforward applicable on the JPEG bitstream (zig-zag ordered quantized DCT coefficients). In this paper, we propose an implementation strategy suitable for single input - single output Takagi-Sugeno fuzzy systems with trapezoidal shaped input membership function directly in the compressed domain. The fuzzy sets parameters are adaptively chosen by analyzing the histogram of the image data in the compressed domain, in order to optimally enhance the image contrast. The fuzzy rule-based algorithm requires some threshold comparisons, for which an adaptive implementation, taking into account the frequency content of each block in the compress domain JPEG image is proposed. This guarantees the minimal error implementation at minimum computational cost.

http://www.wseas.us/e-library/conferences/2008/sofia/FS/fs-22.pdf

Compressed domain implementation of fuzzy rule-based contrast enhancement

An image contrast enhancement algorithms for BDCT (Block Discrete Cosine Transform) based compressed images is presented. Contrast enhancement is achieved by modifying the quantized DCT coefficients based on a contrast measure defined within the DCT domain. The performance is compared with other methods and better visual quality is obtained.

A contrast enhancement method for compressed images

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Kebin An

Author Tools

Chat about Author

Papers

A contrast enhancement method for compressed images