Interpolation based symmetrical predictor structure for lossless image coding
20 May 2012-pp 2913-2916
TL;DR: From performance evaluation, the proposed novel interpolation based prediction scheme is significantly better in terms of compression performance as compared to some of the computationally complex methods.
Abstract: Predictor based algorithms reported in literature uses only causal pixels and hence a non-symmetrical predictor structure for prediction. We observed that the performance of predictor is highly dependent on the predictor structure used. In view of this, we propose a novel interpolation based prediction scheme that enables us to use symmetrical predictor structure. In this sense, we have also used non causal pixels in our scheme. Also, from various interpolation algorithms available, we selected a simple one to ensure decoder simplicity, without any significant loss in performance. From performance evaluation, we found that our algorithm is significantly better in terms of compression performance as compared to some of the computationally complex methods.
Citations
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01 Oct 2014
TL;DR: An additive prediction error expansion (PEE) based reversible data hiding scheme that gives overall low distortion and relatively high embedding capacity and outperforms the state-of-the-art algorithms both in terms of embeddingcapacity and Peak Signal to Noise Ratio.
Abstract: In this paper, we present an additive prediction error expansion (PEE) based reversible data hiding scheme that gives overall low distortion and relatively high embedding capacity. Recently reported interpolation based PEE method uses fixed order predictor that fails to exploit the correlation between the neighborhood pixels and the unknown pixel (to be interpolated). We observed that embedding capacity and distortion of PEE based algorithm depends on the prediction accuracy of the predictor. In view of this observation, we propose an interpolation based method that predicts pixels using predictors of different structure and order. Moreover, we use only original pixels for interpolation. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms both in terms of embedding capacity and Peak Signal to Noise Ratio.
7 citations
01 Jun 2014
TL;DR: A novel two-stage algorithm for lossy, near lossless/lossless compression using a symmetrical predictor structure is proposed and is significantly better in terms of compression performance as compared to some of the computationally complex methods.
Abstract: Prediction based algorithms reported in the literature are not able to integrate lossy and near-lossless/lossless coding and uses only causal pixels (non-symmetrical predictor structure) for prediction. A non-symmetrical predictor structure, however, is not able to efficiently adapt near the intensity varying areas, which results into poor prediction. Hence, we propose a novel two-stage algorithm for lossy, near lossless/lossless compression using a symmetrical predictor structure is proposed. In the first stage, the proposed algorithm encodes and decodes the given image using the JPEG-2000 standard algorithm (lossy coding). This JPEG-2000 decoded image in the first stage, enables us to use the symmetrical predictor (using both causal and non-causal pixels) for prediction in the second stage. A performance evaluation shows that our algorithm is significantly better in terms of compression performance as compared to some of the computationally complex methods.
3 citations
Cites methods from "Interpolation based symmetrical pre..."
...Vinit [10] use interpolation based symmetrical predictor structure (ISPS) for better information exploitation from the neighboring pixels....
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...The proposed symmetrical predictor structure based integrated lossy, near-lossless/lossless coding algorithm is implemented, and its performance for lossless coding is compared with the existing MED, GAP, EDP [5], super-spatial structure prediction (SSP) [8] and ISPS [10] algorithms....
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01 Dec 2012
TL;DR: Experimental results indicates that the proposed algorithm gives better quantitative performance as compared to other conventional interpolation techniques.
Abstract: This paper proposes a new interpolation approach for obtaining high resolution (HR) images from its low resolution (LR) images. We are using the Least Squared based block by block prediction scheme to estimate the predictors using Jacobian iteration method. In spite of Jacobian's Iterative property of convergence for diagonally dominant matrices only, our proposed method uses this property effectively for all types of matrices, and found a set of prediction coefficients using a small number of iterative steps. Due to its lesser computational cost it can be used in real time applications too. Use of iterative methods like Jacobi gives an advantage of its application over images which gives singular matrices during operation. Experimental results indicates that the proposed algorithm gives better quantitative performance as compared to other conventional interpolation techniques.
1 citations
01 Sep 2013
TL;DR: This paper proposes an efficient procedure for removal of salt and pepper noises from the noisy images on the basis of their local edge preserving filters and gives much better qualitative and quantitative performance.
Abstract: This paper proposes an efficient procedure for removal of salt and pepper noises from the noisy images on the basis of their local edge preserving filters. This algorithm consists of two major stages. In the first stage, the maximum and minimum pixel value in the the corrupted image is used to select noisy pixels or noise free pixels and then in second stage, local edge preserving filters are used on the basis of noisy pixel detected and the nature of its neighboring pixels in the selected window. Comparing the obtained results with other computationally simple noise removal techniques, our proposed algorithm gives much better qualitative and quantitative performance. Due to its simplicity and low computational cost, our method is suitable for its application in many real time situations.
1 citations
21 May 2014
TL;DR: An efficient and computationally simple lossless / near-lossless compression algorithm using the bilateral filter and proposes to use both causal and non-causal pixels for bias cancellation.
Abstract: Recently, a few symmetrical predictor structure (SPS) based lossless / near lossless compression algorithms have been proposed, which can efficiently exploit the information from the neighboring pixels. Prediction stage of existing SPS algorithms uses least squares optimization, which is computationally expensive and only causal pixels are used for the bias cancellation stage. In this paper, we propose an efficient and computationally simple lossless / near-lossless compression algorithm using the bilateral filter. Moreover we propose to use both causal and non-causal pixels for bias cancellation. From extensive experiments, it is observed that the proposed algorithm has the capability of provide better prediction and compression performance.
1 citations
Cites background or methods from "Interpolation based symmetrical pre..."
...The existing symmetrical predictor structure (SPS) [9], [10] based algorithms are using the least squares optimization for the prediction, which is computationally expensive and prediction parameters needs to be send as an overhead to the decoder....
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...Unlike, the existing SPS based [9] - [10] algorithms, proposed algorithm only needs to send lossy JPEG2000 encoded image and residual image (e) at the decoder side. e = P − P̂ (5) The near-lossless mode can be enabled by simply quantizing the residual error (e) and using the reconstructed pixels (P̃ ) for the prediction of the future pixels. ê = ⎧⎨ ⎩ ⌊ e+Δ 2Δ+1 ⌋ e ≥ 0⌊ e−Δ 2Δ+1 ⌋ e < 0 (6) Here, Δ controls the trade-off between the compression ratio and the distortion in such a way that |...
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...al [9] proposed a symmetrical predictor structure(SPS) based prediction algorithms which can efficiently exploit the information from the neighboring pixels for better prediction....
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...Unlike, the existing SPS based [9] - [10] algorithms, proposed algorithm only needs to send lossy JPEG2000 encoded image and residual image (e) at the decoder side....
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...However, Vinit et. al [9] proposed a symmetrical predictor structure(SPS) based prediction algorithms which can efficiently exploit the information from the neighboring pixels for better prediction....
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References
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TL;DR: LOCO-I as discussed by the authors is a low complexity projection of the universal context modeling paradigm, matching its modeling unit to a simple coding unit, which is based on a simple fixed context model, which approaches the capability of more complex universal techniques for capturing high-order dependencies.
Abstract: LOCO-I (LOw COmplexity LOssless COmpression for Images) is the algorithm at the core of the new ISO/ITU standard for lossless and near-lossless compression of continuous-tone images, JPEG-LS. It is conceived as a "low complexity projection" of the universal context modeling paradigm, matching its modeling unit to a simple coding unit. By combining simplicity with the compression potential of context models, the algorithm "enjoys the best of both worlds." It is based on a simple fixed context model, which approaches the capability of the more complex universal techniques for capturing high-order dependencies. The model is tuned for efficient performance in conjunction with an extended family of Golomb (1966) type codes, which are adaptively chosen, and an embedded alphabet extension for coding of low-entropy image regions. LOCO-I attains compression ratios similar or superior to those obtained with state-of-the-art schemes based on arithmetic coding. Moreover, it is within a few percentage points of the best available compression ratios, at a much lower complexity level. We discuss the principles underlying the design of LOCO-I, and its standardization into JPEC-LS.
1,668 citations
"Interpolation based symmetrical pre..." refers methods in this paper
...These methods use switched predictors namely Gradient Adaptive Prediction (GAP) [1] and Median Edge Detector (MED) [2], respectively....
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...The proposed prediction algorithm based on symmetrical predictor structure for lossless compression algorithm is implemented, and their performance was compared with existing MED, GAP, EDP and regular mode RALP....
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...The state-of-art prediction based lossless image compression algorithms, include CALIC [1] and JPEG-LS [2]....
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...We have made a complete lossless image coder and compared it with the JPEG LS [2] and CALIC [1]....
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TL;DR: The CALIC obtains higher lossless compression of continuous-tone images than other lossless image coding techniques in the literature and can afford a large number of modeling contexts without suffering from the context dilution problem of insufficient counting statistics as in the latter approach.
Abstract: We propose a context-based, adaptive, lossless image codec (CALIC). The codec obtains higher lossless compression of continuous-tone images than other lossless image coding techniques in the literature. This high coding efficiency is accomplished with relatively low time and space complexities. The CALIC puts heavy emphasis on image data modeling. A unique feature of the CALIC is the use of a large number of modeling contexts (states) to condition a nonlinear predictor and adapt the predictor to varying source statistics. The nonlinear predictor can correct itself via an error feedback mechanism by learning from its mistakes under a given context in the past. In this learning process, the CALIC estimates only the expectation of prediction errors conditioned on a large number of different contexts rather than estimating a large number of conditional error probabilities. The former estimation technique can afford a large number of modeling contexts without suffering from the context dilution problem of insufficient counting statistics as in the latter approach, nor from excessive memory use. The low time and space complexities are also attributed to efficient techniques for forming and quantizing modeling contexts.
1,099 citations
TL;DR: This analysis shows that the superiority of the LS-based adaptation is due to its edge-directed property, which enables the predictor to adapt reasonably well from smooth regions to edge areas.
Abstract: This paper sheds light on the least-square (LS)-based adaptive prediction schemes for lossless compression of natural images. Our analysis shows that the superiority of the LS-based adaptation is due to its edge-directed property, which enables the predictor to adapt reasonably well from smooth regions to edge areas. Recognizing that LS-based adaptation improves the prediction mainly around the edge areas, we propose a novel approach to reduce its computational complexity with negligible performance sacrifice. The lossless image coder built upon the new prediction scheme has achieved noticeably better performance than the state-of-the-art coder CALIC with moderately increased computational complexity.
259 citations
TL;DR: A switching coding scheme that will combine the advantages of both run-length and adaptive linear predictive coding, and uses a simple yet effective edge detector using only causal pixels for estimating the coding pixels in the proposed encoder.
Abstract: Many coding methods are more efficient with some images than others. In particular, run-length coding is very useful for coding areas of little changes. Adaptive predictive coding achieves high coding efficiency for fast changing areas like edges. In this paper, we propose a switching coding scheme that will combine the advantages of both run-length and adaptive linear predictive coding. For pixels in slowly varying areas, run-length coding is used; otherwise least-squares (LS)-adaptive predictive coding is used. Instead of performing LS adaptation in a pixel-by-pixel manner, we adapt the predictor coefficients only when an edge is detected so that the computational complexity can be significantly reduced. For this, we use a simple yet effective edge detector using only causal pixels. This way, the proposed system can look ahead to determine if the coding pixel is around an edge and initiate the LS adaptation in advance to prevent the occurrence of a large prediction error. With the proposed switching structure, very good prediction results can be obtained in both slowly varying areas and pixels around boundaries. Furthermore, only causal pixels are used for estimating the coding pixels in the proposed encoder; no additional side information needs to be transmitted. Extensive experiments as well as comparisons to existing state-of-the-art predictors and coders will be given to demonstrate its usefulness.
34 citations
"Interpolation based symmetrical pre..." refers methods in this paper
...Kau and Lin proposed Run-length and Adaptive Linear Predictive [4] (RALP) coding scheme in which LS based optimization is done (Regular Mode) only when an edge is detected or when prediction error is greater than the predefined threshold....
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26 Aug 2008
TL;DR: The least-squares (LS) based approach to find optimal predictors for pixels belonging to various slope bins of GAP is presented and results show that the proposed method results in similar performance as that of edge directed prediction (EDP) and Run-length and Adaptive Linear Predictive (RALP) coding.
Abstract: Gradient adjusted predictor (GAP), used in CALIC, consists of seven slope bins and one predictor each is associated with these bins. As the relationship between the predicted pixels and their contexts are complex, these predictors may not be appropriate for prediction of the pixels belonging to the respective slope bins. In this work, we present the least-squares (LS) based approach to find optimal predictors for pixels belonging to various slope bins of GAP. Our simulation results show that the proposed method results in similar performance as that of edge directed prediction (EDP) and Run-length and Adaptive Linear Predictive (RALP) coding. EDP and RALP use symmetrical encoder and decoder structure. On the other hand, we propose an unsymmetrical codec that has higher encoding complexity but decoder is very fast - as fast as a decoder based on GAP principle. However, our encoder is computationally much simpler than an EDP and RALP based encoders.
17 citations