Intra Coding With Prediction Mode Information Inference

Laroche, Guillaume; Jung, Joël; Pesquet‐Popescu, Béatrice

doi:10.1109/tcsvt.2010.2087511

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Many studies have tackled the task of improving intraprediction. On one hand, work is being done in efficiently guessing the next prediction mode to be used [6], [7], [8]. If these estimates are correct, then the number of bits needed to describe the next prediction mode can be reduced.…”

Section: Introductionmentioning

confidence: 99%

Regression-based Intra-prediction for Image and Video Coding

Ochotorena,

Yamashita

2016

Preprint

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By utilizing previously known areas in an image, intra-prediction techniques can find a good estimate of the current block. This allows the encoder to store only the error between the original block and the generated estimate, thus leading to an improvement in coding efficiency. Standards such as AVC and HEVC describe expert-designed prediction modes operating in certain angular orientations alongside separate DC and planar prediction modes. Being designed predictors, while these techniques have been demonstrated to perform well in image and video coding applications, they do not necessarily fully utilize natural image structures. In this paper, we describe a novel system for developing predictors derived from natural image blocks. The proposed algorithm is seeded with designed predictors (e.g. HEVC-style prediction) and allowed to iteratively refine these predictors through regularized regression. The resulting prediction models show significant improvements in estimation quality over their designed counterparts across all conditions while maintaining reasonable computational complexity. We also demonstrate how the proposed algorithm handles the worst-case scenario of intra-prediction with no error reporting.

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Section: Introductionmentioning

confidence: 99%

Regression-based Intra-prediction for Image and Video Coding

Ochotorena,

Yamashita

2016

Preprint

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“…A typical mode decision strategy for intra prediction is to estimate the most probable prediction mode by extracting the directional features [13]. Laroche et al [14] improved the intra coding by reducing the bit rate of the intrapredictor indexes along with the distance-based metrics in the DCT domain. Commonly, neighboring spatial information is utilized in a block-based progressive manner [15]–[17].…”

Section: Introductionmentioning

confidence: 99%

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

Dai

Xiong

Jiang

et al. 2013

IEEE Trans. Circuits Syst. Video Technol.

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This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding.

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“…Consequently, none of these intraprediction schemes has been adopted into the HEVC working draft. Finally, Kim et al [18] and Laroche et al [19] looked into another means of improving intracoding by reducing the cost of coding the mode information.…”

Section: Introductionmentioning

confidence: 99%

Intracoding and Refresh With Compression-Oriented Video Epitomic Priors

Wang

2012

IEEE Trans. Circuits Syst. Video Technol.

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In video compression, intracoding plays an important role in terms of coding efficiency and error resilience and has been an attractive research topic since the standardization of H.264/AVC. In this paper, we propose a high-performance intracoding scheme with the help of epitomic priors. Different from intracoding in H.264/AVC and other video standards, we construct image epitomes as coding priors and use them to generate predictions of intrablocks at the encoder. In addition, we losslessly code and transmit the image epitomes to the decoder. We perform compression-oriented video epitomic analysis and search for the best epitomic priors by using the expectation maximization algorithm. The resulting image epitomes for a video sequence can be viewed as the base layer in spatially scalable video coding. Experiments show that our proposed intracoding scheme improves the state of the art by an average of 0.53 dB in PSNR. Simulations under a packet loss environment also demonstrate that intrarefresh with epitomic priors outperforms random intrarefresh by up to 2 dB, leading to better subjective quality.Index Terms-Epitomic analysis, intracoding, intrarefresh, scalable video coding, spatial scalability.

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Intra Coding With Prediction Mode Information Inference

Cited by 5 publications

References 13 publications

Regression-based Intra-prediction for Image and Video Coding

Regression-based Intra-prediction for Image and Video Coding

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

Intracoding and Refresh With Compression-Oriented Video Epitomic Priors

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