To efficiently encode data-intensive multi-view imaging content, conventional hybrid predictive coding methodologies choose to address the compression by exploiting temporal and inter-viewpoint redundancy. However, th...
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To efficiently encode data-intensive multi-view imaging content, conventional hybrid predictive coding methodologies choose to address the compression by exploiting temporal and inter-viewpoint redundancy. However, their key yet time-consuming component, motion estimation (ME), is usually not efficient in inter-viewpoint prediction because inter-viewpoint motion is quite different from temporal motion. In essence, inter-viewpoint correlation is subject to epipolar geometry, which provides constraints for multi-view image sequences. A fast inter-viewpoint ME technique is hence proposed in this paper to accelerate the encoding by employing epipolar geometry. Theoretical analysis and experimental results prove that the proposed ME algorithm can greatly reduce search region and effectively track large and irregular motion that is typical for convergent multi-view camera setups. As a result, compared with fast full search at large search size adopted in H.264, our proposed ME algorithm can obtain a similar coding efficiency while achieving a speedup ratio of 2.9.
The frequency selective extrapolation extends an image signal beyond a limited number of known samples. This problem arises in image and video communication in error prone environments where transmission errors may le...
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The frequency selective extrapolation extends an image signal beyond a limited number of known samples. This problem arises in image and video communication in error prone environments where transmission errors may lead to data losses. In order to estimate the lost image areas, the missing pixels are extrapolated from the available correctly received surrounding area which is approximated by a weighted linear combination of basis functions. In this contribution, we integrate the frequency selective extrapolation into the H.264/AVC coder as spatial concealment method. The decoder reference software uses spatial concealment only for I frames. Therefore, we investigate the performance of our concealment scheme for I frames and its impact on following P frames caused by error propagation due to predictive coding. Further, we compare the performance for coded video sequences in TV quality against the non-normative concealment feature of the decoder reference software. The investigations are done for slice patterns causing chequerboard and raster scan losses enabled by flexible macroblock ordering (FMO).
We investigate several problems in scanning of multidimensional data arrays, such as universal scanning and prediction ("scandiction", for short), and scandiction of noisy data arrays. These problems arise i...
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ISBN:
(纸本)142440505X
We investigate several problems in scanning of multidimensional data arrays, such as universal scanning and prediction ("scandiction", for short), and scandiction of noisy data arrays. These problems arise in several aspects of image and video processing, such as predictive coding, filtering and denoising. In predictive coding of images, for example, an image is compressed by coding the prediction error sequence resulting from scandicting it. Thus, it is natural to ask what is the optimal method to scan and predict a given image, what is the resulting minimum prediction loss, and if there exist specific scandiction schemes which are universal in some sense. More specifically, we investigate the following problems: first, given a random field, we examine whether there exists a scandiction scheme which is independent of the field's distribution, yet asymptotically achieves the same performance as if this distribution was known. This question is answered in the affirmative for the set of all spatially stationary random fields and under mild conditions on the loss function. We then discuss the scenario where a non-optimal scanning order is used, yet accompanied by an optimal predictor, and derive a bound on the excess loss compared to optimal scandiction. Finally, we examine the scenario where the random field is corrupted by noise, but the scanning and prediction (or filtering) scheme is judged with respect to the underlying noiseless field
The multidimensional multiscale parser (MMP) is a lossy multidimensional signal encoder, that uses an adaptive dictionary for approximating the original signal using multiscale recurrent pattern matching. In previous ...
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ISBN:
(纸本)9781424436446;8589748049
The multidimensional multiscale parser (MMP) is a lossy multidimensional signal encoder, that uses an adaptive dictionary for approximating the original signal using multiscale recurrent pattern matching. In previous work we have shown the efficiency of MMP for image coding and we have also described new techniques to improve its performance, using predictive coding (MMP-Intra) and innovative strategies for reducing the dictionary redundancy. The combination of these methods for image coding achieves much better results than the state-of-the-art JPEG2000 and H.264/AVC Intra image encoders for text and compound images, but for smooth natural images it still presents small losses. In this work we present a new technique to improve the dictionary adaptation process of the MMP-Intra, based on enhanced updating techniques. Experimental results have showed that, when combined with dictionary growth control methods, this technique achieves consistent image quality gains for all image types. Furthermore we present some methods that eliminate the intrinsic substantial increase of the computational complexity associated with more rapidly growing dictionaries, without compromising the final quality of the decoded image.
In predictive image coding, the least squares (LS)-based adaptive predictor is noted as an efficient method to improve prediction result around edges. However pixel-by-pixel optimization of the predictor coefficients ...
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In predictive image coding, the least squares (LS)-based adaptive predictor is noted as an efficient method to improve prediction result around edges. However pixel-by-pixel optimization of the predictor coefficients leads to a high coding complexity. To reduce computational complexity, we activate the LS optimization process only when the coding pixel is around an edge or when the prediction error is large. We propose a simple yet effective edge detector using only causal pixels. The system can look ahead to determine if the coding pixel is around an edge and initiate the LS adaptation to prevent the occurrence of a large prediction error. Our experiments show that the proposed approach can achieve a noticeable reduction in complexity with only a minor degradation in the prediction results
This paper presents a new generalized particle model (GPM) to generate the prediction coding for lossless data compression. Local rules for particle movement in GPM, parallel algorithm and its implementation structure...
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This paper presents a new generalized particle model (GPM) to generate the prediction coding for lossless data compression. Local rules for particle movement in GPM, parallel algorithm and its implementation structure to generate the desired predictive coding are discussed. The proposed GPM approach has advantages in terms of encoding speed, parallelism, scalability, simplicity, and easy hardware implementation over other sequential lossless compression methods
A method for amplitude modulated sinusoidal audio coding is presented that has low complexity and low delay. This is based on a sub-band processing system, where, in each subband, the signal is modeled as an amplitude...
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A method for amplitude modulated sinusoidal audio coding is presented that has low complexity and low delay. This is based on a sub-band processing system, where, in each subband, the signal is modeled as an amplitude modulated sum of sinusoids. The envelopes are estimated using frequency-domain linear prediction and the prediction coefficients are quantized. As a proof of concept, we evaluate different configurations in a subjective listening test, and this shows that the proposed method offers significant improvements in sinusoidal coding. Furthermore, the properties of the frequency-domain linear prediction-based envelope estimator are analyzed
In this paper we propose a new adaptive prediction scheme based on the blending of multiple static predictors on a dynamically classified causal context of neighboring pixels. The idea of predictor blends is further e...
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In this paper we propose a new adaptive prediction scheme based on the blending of multiple static predictors on a dynamically classified causal context of neighboring pixels. The idea of predictor blends is further expanded through the determination of blending context that changes its shape on a pixel-by-pixel basis using a simple classification technique, thus allowing the modeling of more complex image structures such as nontrivially oriented edges, the periodicity and the coarseness of textures. Typical natural images are characterized as being composed of image regions with different local properties. Proposed predictor estimates those properties around the currently unknown pixel and adjusts itself so that the presence of detected properties affects the way final prediction is made
In this paper, we propose a new seamless bitstream switching scheme to improve the coding performance of H.264 SP-frames for rate adaptation. Our method removes one of the two re-quantization blocks in the SP-frame en...
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In this paper, we propose a new seamless bitstream switching scheme to improve the coding performance of H.264 SP-frames for rate adaptation. Our method removes one of the two re-quantization blocks in the SP-frame encoders so as to significantly improve coding performance. The seamless switching property of SP-frames is retained by properly restructuring the primary and secondary switching frame codecs. Experimental results show that our proposed scheme achieves close coding performance to that of regular H.264 P-frames and significantly better performance than that of SP-frames. The proposed method also provides the advantage of using a single secondary switching bitstream for both switching-up and switching-down processes.
This paper presents a new generalized particle model (GPM) to generate the prediction coding for lossless data compression. We discuss the GPM-based parallel algorithm, its properties and realization scheme. The propo...
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This paper presents a new generalized particle model (GPM) to generate the prediction coding for lossless data compression. We discuss the GPM-based parallel algorithm, its properties and realization scheme. The proposed GPM approach has advantages in terms of parallelism, scalability and easy hardware implementation over other sequential lossless compression methods
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