Prediction of signs of DCT coefficients in block-based lossy image compression

Ponomarenko, Nikolay N.; Bazhyna, A.; Егиазарян, Карен

doi:10.1117/12.713872

Cited by 7 publications

(13 citation statements)

References 14 publications

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“…Tu and Tran [4] proposed a technique inspired by sub-band coding in which DCT coefficients of specific frequency bands are collected in new blocks, and the sign bits are then compressed efficiently by exploiting their correlation in a new block, as was done in EBCOT [5]. Ponomarenko et al [6] proposed a two-stage technique, where the pixel values of a target block are first estimated from those of already encoded and decoded neighbor blocks and then used to predict the sign bits of DCT coefficients. Since there is a possibility that several predicted sign bits will be incorrect, they proposed transmitting the residual information, i.e., the prediction error, comprising compressible binaries.…”

Section: Motivationmentioning

confidence: 99%

“…Our method relies on a classical signal processing method, referred to as phase retrieval (PR) [11] detailed in Section 2. Specifically, we first retrieve the sign information of all the AC components in the cosine domain only from their magnitudes, and the encoder and decoder then respectively compose and parse a tailored bitstream that includes the residual information to compensate for errors in the retrieved signs, as was done in [6]. To the best of our knowledge, this is the first paper that incorporates the PR methodology into image compression.…”

Section: Motivationmentioning

confidence: 99%

“…We then apply the CS theory [13] to (6) because of its illposedness from the sampling viewpoint: the DFT coefficients are oversampled in (2) while the number of DCT coefficients is equivalent to that of the pixels in (6). The theory claims that a sparse signal can be restored from a set of few measurements by solving a constrained ℓ 1 -norm minimization problem.…”

Section: Sign Retrieval and Its Convex Relaxationmentioning

confidence: 99%

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An Efficient Compression Method For Sign Information Of DCT Coefficients Via Sign Retrieval

Tsutake

Takahashi

Fujii

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Compression of the sign information of discrete cosine transform coefficients is an intractable problem in image compression schemes due to the equiprobable occurrence of the sign bits. To overcome this difficulty, we propose an efficient compression method for such sign information based on phase retrieval, which is a classical signal restoration problem attempting to find the phase information of discrete Fourier transform coefficients from their magnitudes. In our compression strategy, the sign bits of all the AC components in the cosine domain are excluded from a bitstream at the encoder and are complemented at the decoder by solving a sign recovery problem, which we call sign retrieval. The experimental results demonstrate that the proposed method outperforms previous techniques for sign compression in terms of a rate-distortion criterion. Our method implemented in Python language is available from https://github.com/ctsutake/sr.

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Sign Retrieval and Its Convex Relaxationmentioning

confidence: 99%

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An Efficient Compression Method For Sign Information Of DCT Coefficients Via Sign Retrieval

Tsutake

Takahashi

Fujii

2021

2021 IEEE International Conference on Image Processing (ICIP)

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“…In [20], a similar approach was described where an entropy-coded flag indicates whether a sign is correctly predicted or not. It resulted in increasing compression ratio of 3-9 that corresponds to up to 0.5 dB improvement in PSNR [20].…”

Section: A) Transform Coefficients Prediction Techniquesmentioning

confidence: 99%

“…In this case, Laplacian operator was used as a smoothness cost function that allowed the authors to decrease bit-rate of JPEG-coded pictures by 0.1–0.2 bit/pixel on average [19]. In [20], a similar approach was described where an entropy-coded flag indicates whether a sign is correctly predicted or not. It resulted in increasing compression ratio of 3–9% that corresponds to up to 0.5 dB improvement in PSNR [20].…”

Section: Overviewmentioning

confidence: 99%

Residual sign prediction in transform domain for next-generation video coding

Filippov¹,

Rufitskiy²,

Karabutov³

et al. 2019

SIP

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In this paper, we present a technique that is known as Residual Sign Prediction in Transform Domain (TDRSP) and is aimed at increasing compression performance by reducing the bits overhead of residue sign. These signs are typically coded by entropy coders in bypass mode that results in the high cost of sign bins which require 1 bit per bin in a bitstream. TDRSP allows us to reduce this cost by predicting residue signs so that the probability of a guess is significantly higher than 50%. Hence, arithmetic coding with contexts becomes applicable to the signs. In contrast to Residual Sign Prediction (RSP) performed in spatial domain, TDRSP avoids switching between domains and carries out calculations completely in transform domain to efficiently decrease the computational complexity of RSP. Simulations performed on top of Versatile Video Coding test model reference software (VTM-1.0) in accordance with the Joint Video Experts Team common test conditions show that more than 2.0% and up to 1.8% of the Bjøntegaard Delta rate can be achieved for All Intra and Random Access configurations, respectively.

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An efficient sign prediction method for DCT coefficients and its application to reversible data embedding in scrambled JPEG image

Rad

Wong

2013

2013 IEEE International Conference on Image Processing

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In this paper, an efficient DCT sign prediction method is proposed. Unlike the conventional methods that depend on information from both spatial and frequency domains, the proposed method operates solely in the frequency domain by exploiting the pixel value patterns represented by the corresponding DCT basis vectors. In particular, each block is classified into five categories, namely, complexpattern, complex-nonpattern, simple-smooth, simple-pattern and simple-texture, and each is treated differently using the proposed predictor. The proposed sign prediction method is then applied to realize reversible data embedding using sign information in a scrambled JPEG compressed image. This work is the first of its kind in using sign information for data embedding purposes. Basic performance of the proposed sign prediction and the proposed reversible data embedding method in scrambled JPEG image are verified using standard test images.

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Prediction of signs of DCT coefficients in block-based lossy image compression

Cited by 7 publications

References 14 publications

An Efficient Compression Method For Sign Information Of DCT Coefficients Via Sign Retrieval

An Efficient Compression Method For Sign Information Of DCT Coefficients Via Sign Retrieval

Residual sign prediction in transform domain for next-generation video coding

An efficient sign prediction method for DCT coefficients and its application to reversible data embedding in scrambled JPEG image

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