Recompression of JPEG images by requantization

Bauschke, Heinz H.; Hamilton, Chris; Macklem, Mason S.; McMichael, Jonathan; Swart, Nicholas

doi:10.1109/tip.2003.812375

Cited by 48 publications

(41 citation statements)

References 4 publications

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“…Another branch of studies focused on further compression of JPEG images rather than the quality improvement [9,17,18,19,20,21]. Some of these studies [18,19,20] approached a lossless manner for further compression as the proposed method does while the other tried to make further compression in a lossy manner.…”

Section: Background and Related Studymentioning

confidence: 99%

“…Some of these studies [18,19,20] approached a lossless manner for further compression as the proposed method does while the other tried to make further compression in a lossy manner. One research attempted to achieve better compression ratio along with better quality [9] although it changes Baseline JPEG algorithm considerably.…”

Section: Background and Related Studymentioning

confidence: 99%

“…For color images, the color planes are separated before the algorithm is applied and the RGB color domain is transformed to Y C b C r color domain in order to separate the luminance and chrominance signals [7]. The separation of color space is mandatory for color images, but transformation to Y C b C r color domain in addition has been kept optional [8,9].…”

Section: Introductionmentioning

confidence: 99%

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An Improved JPEG Image Compression Technique based on Selective Quantization

Hasan¹,

Nur²,

Shakur³

2012

IJCA

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In today's communicative and multimedia computing world, JPEG images play a vast consequential role. The JPEG images have been able to satisfy the users by fulfilling their demand of preserving numerous digital images within considerably less storage space. Although the JPEG standard offers four different sorts of compression mechanism, among them the Baseline JPEG or Lossy Sequential DCT Mode of JPEG is most popular since it can store a digital image by temporarily removing its psychovisual redundancy and thereby offering a very less storage space for a large image. Again, the computational complexity of Baseline JPEG is also considerably less as compression takes place in Discrete Cosine Transform domain. Therefore, Baseline JPEG is substantially useful while storing, sharing and transmitting digital images. Despite removing a large amount of psychovisual redundancy, the Baseline JPEG still contains redundant data in DCT domain. This paper explores the fact and introduces an improved technique that modifies the Baseline JPEG algorithm. It describes a way to further compress a JPEG image without any additional loss while achieving a better compression ratio than that is achievable by Baseline JPEG. The contribution of this work is to incorporate a simple mathematical series with Baseline JPEG before applying optimal encoding and perform a Selective Quantization that essentially does not loss any information after decompression but reduces the redundant data in DCT domain. The proposed technique is tested on over 200 textbook images that are extensively used for testing standard Image Processing and Computer Vision algorithms. The experimental results show that our proposed approach achieves 2.15% and 14.10% better compression ratio than that is achieved by Baseline JPEG on an average for gray-scale and true-color images respectively. General Terms:JPEG, Huffman Coding

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Section: Background and Related Studymentioning

confidence: 99%

Section: Background and Related Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved JPEG Image Compression Technique based on Selective Quantization

Hasan¹,

Nur²,

Shakur³

2012

IJCA

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“…, Q k } comprises the compression history of the image, we refer to this problem as JPEG CHEst. An image's compression history is useful for applications such as JPEG recompression [4,8,9].…”

Section: Jpeg Chest Problem Statementmentioning

confidence: 99%

On Nearly Orthogonal Lattice Bases and Random Lattices

Neelamani

Dash

Baraniuk

2007

SIAM J. Discrete Math.

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We study "nearly orthogonal" lattice bases, or bases where the angle between any basis vector and the linear subspace spanned by the other basis vectors is greater than π 3 radians. We show that a nearly orthogonal lattice basis always contains a shortest lattice vector. Moreover, if the lengths of the basis vectors are "nearly equal", then the basis is the unique nearly orthogonal lattice basis, up to multiplication of basis vectors by ±1. These results are motivated by an application involving JPEG image compression.

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“…Werner [1] investigated the requantization problem to transcode MPEG-2 I-frame, where the transcoding problem is analyzed theoretically, and an efficient requantizer based on the analysis is derived. In [2] AC DCT coefficients. Nakajima et al [3] determined the requantization step size to convert a high bit rate MPEG stream into the lower bit rate one by using the activity in the spatial domain.…”

mentioning

confidence: 99%

Optimization of requantization codebook for vector quantization

Han

Kim

2006

IEEE Trans. on Image Process.

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Abstract-Requantization is a key technology for reducing the bit rate of a previously compressed data. When recompression ratio is high, the requantizer may cause unacceptable quality degradation. To improve the quality of the requantized image, an optimization scheme for the requantization codebook has been proposed. The proposed scheme constructs an optimal requantization codebook in an iterative manner for a given original quantization codebook of transmitter. The construction of codebook is iteratively repeated until they reach a local optimum solution. Our approach can be applied not only to the scalar quantization, but to any method which employs vector quantization-based system. Simulation results show that the optimized system based on the proposed algorithm outperforms the conventional system which is made without consideration of requantization. The proposed algorithm enables a reliable image communication over heterogeneous networks. Index Terms-Requantization, scalar quantization (SQ), vector quantization (VQ).

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Recompression of JPEG images by requantization

Cited by 48 publications

References 4 publications

An Improved JPEG Image Compression Technique based on Selective Quantization

An Improved JPEG Image Compression Technique based on Selective Quantization

On Nearly Orthogonal Lattice Bases and Random Lattices

Optimization of requantization codebook for vector quantization

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