Image compression scheme based on zig-zag 3D-DCT and LDPC coding

Mulla, Afshan; Baviskar, Jaypal; Baviskar, Amol; Warty, Chirag

doi:10.1109/icacci.2014.6968373

Cited by 16 publications

(10 citation statements)

References 12 publications

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“…Table I provides the time required for segmenting the image according to user input and assigning the cluster heads accordingly. It is observed that the, parallel processing and clustering improves the processing time compared to [9,10]. However, as the number of segments increases the complexity in assigning the cluster heads increases which in turn results into the excessive amount of time consumption and hence degrades the performance of the algorithm.…”

Section: A Analysis Of Processing Time Of the Algorithmmentioning

confidence: 90%

“…The quality parameters for various images have been evaluated. It is observed that the, parallel processing and clustering improves the processing time without hampering the quality of image compared to [9,10].…”

Section: A Analysis Of Processing Time Of the Algorithmmentioning

confidence: 92%

“…ZigZag Scanning technique-It has been a popular scanning scheme since it formed the base for the preprocessing stage in JPEG standard. In JPEG, the ZigZag scanning is used along with 2D-DCT; this technique for 3D-DCT is been implemented and described in detail in Afshan et al [9]. As depicted in Figure. 3; to cover the entire area it starts from the top-most left corner (first pixel) or a first block and go on scanning in ZigZag manner towards the bottom most right corner (last pixel) or a last block.…”

Section: A Sequential Scanning Techniquementioning

confidence: 99%

“…total processing time for an image, segmentation and cluster assigning time for different size of images as a function of number of segments are illustrated in this section. It also briefly illustrates the quality metrics which are elaborated in detail in [9,10].…”

Section: Performance Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

High-speed cluster scanning scheme for three-dimensional constellated image processing

Mulla¹,

Baviskar²,

Baviskar

et al. 2015

2015 IEEE Aerospace Conference

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Due to the Human Visual System (HVS) the response to well brightened images is frequency dependent. The Discrete Cosine Transform (DCT) -based compression schemes are capable of separating the perceptually significant information in an image from the information that the eye cannot perceive. Hence in image compression, DCT is most widely used transform. In this paper, an algorithm for compressing images based on Cluster 3D-DCT technique is proposed. This method initially segments the two-dimensional image and then forms the clusters within each segment. These segments are then parallely processed to form a three-dimensional cube of 8*8*8 pixels and is processed with DCT. Thereafter the quantization and zigzag scanning processes are implemented. After completing the processes, the 1D data vector formed facilitates in achieving better compression using run-length coding. The performance of the algorithm is verified by plotting graphs, and determining its dominance over other primitive scanning techniques. The processing time and time required to segment the image along with cluster assignment time is tabulated. The cluster scanning which is a parallel progression proves to be faster compared to Spiral, ZigZag and Interleaving scanning schemes.

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Section: A Analysis Of Processing Time Of the Algorithmmentioning

confidence: 90%

Section: A Analysis Of Processing Time Of the Algorithmmentioning

confidence: 92%

Section: A Sequential Scanning Techniquementioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

High-speed cluster scanning scheme for three-dimensional constellated image processing

Mulla¹,

Baviskar²,

Baviskar

et al. 2015

2015 IEEE Aerospace Conference

Self Cite

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“…The algorithm performs better than the algorithm illustrated in various papers such as[3][4][5][6].Fig.10illustrates the PSNR denouement between original with respect to recovered color image, considering two cases; viz. 2-level Sub-band replacement method and 4-level Subband replacement method.…”

mentioning

confidence: 92%

Enhanced quality LANDSAT image processing based on 4-level Sub-Band Replacement DWT

Mulla¹,

Baviskar²,

Naik³

et al. 2015

2015 IEEE Aerospace Conference

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Satellite images are multi-spectral in nature that operate over wavelengths of wide range frequencies known as bands. Hence, they need to be processed cautiously. This paper presents a 4-Level DWT based Sub-Band Replacement (SR-DWT) Image Compression scheme, for LANDSAT satellite images. The algorithm is designed to compress various color band images captured by the satellite payload. By performing compression on these images and converting them to unique textured images, it exploits the sub-bands generated by the wavelet transform. Since the algorithm is a reversible process, it facilitates retrieval of the images back to colored format at the receiver. Due to the property of possessing spatial-frequency decomposition, the recovered images are of high quality with improved Peak Signal to Noise Ratio (PSNR) of 20dB and correspondingly very low MSE value. Using an astrophysical image database, the performance analysis of the 4-Level SR-DWT algorithm is illustrated.

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Video compression based on zig-zag 3D DCT and run-length encoding for multimedia communication systems

Chutke

N.M.

Lendale

2022

IJPCC

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Purpose With the advent of technology, a huge amount of data is being transmitted and received through the internet. Large bandwidth and storage are required for the exchange of data and storage, respectively. Hence, compression of the data which is to be transmitted over the channel is unavoidable. The main purpose of the proposed system is to use the bandwidth effectively. The videos are compressed at the transmitter’s end and reconstructed at the receiver’s end. Compression techniques even help for smaller storage requirements. Design/methodology/approach The paper proposes a novel compression technique for three-dimensional (3D) videos using a zig-zag 3D discrete cosine transform. The method operates a 3D discrete cosine transform on the videos, followed by a zig-zag scanning process. Finally, to convert the data into a single bit stream for transmission, a run-length encoding technique is used. The videos are reconstructed by using the inverse 3D discrete cosine transform, inverse zig-zag scanning (quantization) and inverse run length coding techniques. The proposed method is simple and reduces the complexity of the convolutional techniques. Findings Coding reduction, code word reduction, peak signal to noise ratio (PSNR), mean square error, compression percent and compression ratio values are calculated, and the dominance of the proposed method over the convolutional methods is seen. Originality/value With zig-zag quantization and run length encoding using 3D discrete cosine transform for 3D video compression, gives compression up to 90% with a PSNR of 41.98 dB. The proposed method can be used in multimedia applications where bandwidth, storage and data expenses are the major issues.

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Image compression scheme based on zig-zag 3D-DCT and LDPC coding

Cited by 16 publications

References 12 publications

High-speed cluster scanning scheme for three-dimensional constellated image processing

High-speed cluster scanning scheme for three-dimensional constellated image processing

Enhanced quality LANDSAT image processing based on 4-level Sub-Band Replacement DWT

Video compression based on zig-zag 3D DCT and run-length encoding for multimedia communication systems

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