Compressed domain editing of JPEG2000 images

Chebil, Fehmi; Miled, M.K.B.H.; Islam, Ashraful; Willner, Konrad

doi:10.1109/tce.2005.1468023

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…In [31] text line segmentation on handwritten document images is performed directly in Run-Lenth compressed domain. Some of works have been reported for image processing operations directly in JPEG 2000 compressed domain [32] using DWT properties. They have used techniques to modify the image and perform scalar addition, multiplication adding two images, directly in compressed domain.…”

Section: Introductionmentioning

confidence: 99%

Segmentation-Less Extraction of Text and Non-Text Regions From JPEG 2000 Compressed Document Images Through Partial and Intelligent Decompression

et al. 2023

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JPEG 2000 is a popular image compression technique that uses Discrete Wavelet Transform (DWT) for compression and subsequently provides many rich features for efficient storage and decompression. Though compressed images are preferred for archival and communication purposes, their processing becomes difficult due to the overhead of decompression and re-compression operations which are needed as many times the data needs to operate. Therefore in this research paper, the novel idea of direct operation over the JPEG 2000 compressed documents is proposed for extracting text and non-text regions without using any segmentation algorithm. The technique avoids full decompression of the compressed document in contrast to the conventional methods, where they fully decompress and then process. Moreover, JPEG 2000 features are explored in this research work to partially and intelligently decompress only the selected regions of interest at different resolutions and bitdepths to accomplish segmentation-less extraction of text and non-text regions. Finally Maximally Stable Extremal Regions (MSER) algorithm is used to extract the layout of segmented text and non-text regions for further analysis. Experiments have been carried out on the standard PRImA Layout Analysis Dataset leading to promising results and saving computational resources.

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Section: Introductionmentioning

confidence: 99%

Segmentation-Less Extraction of Text and Non-Text Regions From JPEG 2000 Compressed Document Images Through Partial and Intelligent Decompression

et al. 2023

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“…In the literature, compressed streams of various file formats have been analyzed, and different segmentation techniques are reported directly on the compressed streams [18]. Some of them are like Run length [2,18], JPEG [8,19,35], JBIG [28,39], JPEG2000 [6,15] and Fast H.264 [44]. Run length compressed domain approaches for text-line, Word and character segmentation are reported in [16,17].…”

Section: Introductionmentioning

confidence: 99%

FastSS: Fast and smooth segmentation of JPEG compressed printed text documents using DC and AC signal analysis

Rajesh

Javed

Nagabhushan

2022

Multimed Tools Appl

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With the surge of COVID-19 pandemic, the world is moving towards digitization and automation more than it was presumed. The Internet is becoming one of the popular mediums for communication, and multimedia (image, audio, and video) combined with data compression techniques play a pivotal role in handling a huge volume of data that is being generated on a daily basis. Developing novel algorithms for automatic analysis of compressed data without decompression is the need of the present hour. JPEG is a popular compression algorithm supported in the digital electronics world that achieves compression by dividing the whole image into non-overlapping blocks of 8 × 8 pixels, and subsequently transforming each block using Discrete Cosine Transform (DCT). This research paper proposes to carry out Fast and Smooth Segmentation (FastSS) directly in JPEG compressed printed text document images at text-line and word-level using DC and AC signals. From each 8 × 8 block, DC and AC signals are analyzed for accomplishing Fast and Smooth segmentation, and subsequently, two Faster segmentation (MFastSS) algorithms are also devised using low resolution-images generated by mapping the DC signal (DC Reduced Image) and encoded DCT (ECM Image) coefficients separately. Proposed models are tested on various JPEG compressed printed text document images created with varied space and fonts. The experimental results have demonstrated that the direct analysis of compressed streams is computationally efficient, and has achieved speed gain more than 90% when compared to uncompressed domains.

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“…Motivated by [6], wherein some techniques are presented to achieve common editing and processing operations on images in compressed form, we propose a low complexity image watermarking algorithm for JPEG2000 images by using the information content (IC). First, the codestream is scanned to obtain the IC's, defined in [7], of all the subbands by interpreting the relevant packet headers information.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Blind Watermarking for JPEG2000 Compression Domain

Zhao

Liu

Ren

2009

2009 2nd International Congress on Image and Signal Processing

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This paper presents an adaptive JPEG2000 compressed domain watermarking algorithm wherein the rich codestream syntax of the JPEG2000 standard is fully utilized. Firstly, the watermark embedding location is determined by the information contents (IC's for short) of the wavelet subbands, which is defined as the numbers of bytes that JPEG2000 spends to encode corresponding subbands. Then, a reference significance map template is defined by utilizing the features of the relevant subbands. Finally, the security of this system is strengthened by the ARNOLD transform of the binary watermark images. Incidentally, the watermark is extracted without the original image. Experimental results show the superior performance of the scheme against common image operations while basically preserving the file size.

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Compressed domain editing of JPEG2000 images

Cited by 4 publications

References 8 publications

Segmentation-Less Extraction of Text and Non-Text Regions From JPEG 2000 Compressed Document Images Through Partial and Intelligent Decompression

Segmentation-Less Extraction of Text and Non-Text Regions From JPEG 2000 Compressed Document Images Through Partial and Intelligent Decompression

FastSS: Fast and smooth segmentation of JPEG compressed printed text documents using DC and AC signal analysis

Adaptive Blind Watermarking for JPEG2000 Compression Domain

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