This report proposes a new lossless coding of images. The decoder can expand a rough image from a part of the bit stream (compressed data) and also it can expand the original image from the rest. This functionality is...
详细信息
This report proposes a new lossless coding of images. The decoder can expand a rough image from a part of the bit stream (compressed data) and also it can expand the original image from the rest. This functionality is useful for progressive transmission of images or browsing images in a huge database. The new method is based on a reversible wavelet (RWT) and a lossless multi-channel prediction (LMP). Filter coefficients of the LMP are optimized for each input image so that the total bit rate is minimized. Experimental results indicate its effectiveness for use of lossless image coding. Entropy rate is reduced by 0.35 bpp in average for some images.
A two-stage multiple description predictive coding technique is proposed. A predictive encoder is used in the first stage of each description. The two encoders are designed to create staggered quantizers for the input...
详细信息
A two-stage multiple description predictive coding technique is proposed. A predictive encoder is used in the first stage of each description. The two encoders are designed to create staggered quantizers for the input, which allow further refinement of the joint reconstruction in the second stage. Theoretical analysis and simulation results show that this method is very efficient in the high rate multiple description coding of strongly correlated sources.
Attention-based models have achieved outstanding performance in sequential recommendation due to their ability to obtain better sequential representations. However, these models still suffer from over-fitting and over...
详细信息
Attention-based models have achieved outstanding performance in sequential recommendation due to their ability to obtain better sequential representations. However, these models still suffer from over-fitting and over-parameterization issues. Regularization techniques such as dropout can alleviate the over-fitting problem, but an excessively high dropout rate means a greater probability of missing sequential critical relationships. This will lead to incomplete user representations and sub-optimal model performance. In addition, the single embedding of timestamps also leads to incomplete user representation. Therefore, we introduce a Multi-view Attention Network with Contrastive predictive coding (MVACPC) to solve the above problems in this paper. Specifically, we design a multi-view attention network to alleviate the problem of dropouts losing critical information in the sequence, which can obtain more comprehensive user representations. The network is based on the idea of parallel computing and parameter sharing without adding too much training time and parameters. In order to make the model learn more temporal information, We use different temporal encoding methods for multiple positive sample sequences obtained through data augmentation. Finally, we devise an extended contrastive predictive coding loss to optimize the model. We validate the effectiveness of the MVACPC on public datasets, and the experimental results show that MVACPC outperforms various state-of-the-art sequential recommendation models on different evaluation metrics.
Based on the relationship among the peak points and valley points of the probability density function (p.d.f.) of a stochastic process, whose p.d.f. may be multimodal, the drift coefficient of its associated diffusion...
详细信息
Based on the relationship among the peak points and valley points of the probability density function (p.d.f.) of a stochastic process, whose p.d.f. may be multimodal, the drift coefficient of its associated diffusion process, the 'shift back to center' property of the Markov chain and the state transitive value of the chain, the paper introduces the algorithm for constructing the approximating model of the Markov chain of an Ito stochastic differential equation (AMMC). The results of simulations demonstrate that the variance of the prediction error of the AMMC is not only far smaller than that of the Burg lattice predictor, but also very close to constant. These properties of the algorithm are beneficial to predictor and predictive coding.
The neurobiological predictive coding model proposed by Rao and Ballard is one of the most well-known and carefully tested models in the current research space. The manifestation of predictive coding in animals’ visu...
详细信息
The neurobiological predictive coding model proposed by Rao and Ballard is one of the most well-known and carefully tested models in the current research space. The manifestation of predictive coding in animals’ visual cortices(such as cats and monkeys) has been adequately demonstrated; however, due to the lack of analytical equipment for the nuanced study of the human brain, it has not been demonstrated comparably in humans. Recently there has been an increase in the variety of opinions in neurobiology research about the application of machine learning/artificial intelligence to understand further and investigate predictive coding theory. In this paper, we induce the predictive coding neural network model (PredNet) into an adversarial setting of Wasserstein and the Conditional-Wasserstein nature. Our experiment includes approximately 60 combinatorial variants of the neural networks and two datasets. The results from our experiments seem to substantiate a new perspective on predictive coding theory. In addition to presenting a unique perspective through our research in this paper, we also provide the performance profile of PredNet in conjunction with an adversarial setting through extensive experimentation and analysis.
In this paper a waveform coder configuration based on non linear adaptive prediction will be described. The coder is based on the characteristic of Volterra predictors to model non linear phenomena and to gather infor...
详细信息
In this paper a waveform coder configuration based on non linear adaptive prediction will be described. The coder is based on the characteristic of Volterra predictors to model non linear phenomena and to gather informations about the periodicity of the signal via high order statistical moments. The main result is that, by using this type of predictor, lower variance error signals can be obtained, as compared to the classical, linear, case.
Gaussian mixture model (GMM)-based predictive coding of line spectral frequencies (LSFs) has gained wide acceptance. In such coders, each mixture of a GMM can be interpreted as defining a linear predictive transform c...
详细信息
Gaussian mixture model (GMM)-based predictive coding of line spectral frequencies (LSFs) has gained wide acceptance. In such coders, each mixture of a GMM can be interpreted as defining a linear predictive transform coder. In this paper we optimize each of these linear predictive transform coders using Kalman predictive coding techniques to present GMM Kalman predictive coding. In particular, we show how suitable modeling of quantization noise leads to an adaptive a-posteriori GMM that defines a signal-adaptive predictive coder that provides superior coding of LSFs in comparison with the baseline GMM predictive coder. Moreover, we show how running the Kalman predictive coders to convergence can be used to design a stationary predictive coding system which again provides superior coding of LSFs but now with no increase in run-time complexity over the baseline.
The invertible grayscale technique possesses excellent color restorability by implicitly encoding color information. However, the color-encoded texture patterns are susceptible to external image manipulations, such as...
详细信息
ISBN:
(数字)9798350374186
ISBN:
(纸本)9798350374193
The invertible grayscale technique possesses excellent color restorability by implicitly encoding color information. However, the color-encoded texture patterns are susceptible to external image manipulations, such as JPEG compression, which limits their practical applications. One natural approach is to introduce disturbances during training to learn a robust encoding scheme. Nevertheless, it is challenging to maintain both grayscale visual quality and the robustness of color restorability in the encoding scheme. This means that the color-encoded texture patterns may become amplified. To address this, we propose a novel approach inspired by predictive coding, where we utilize a pre-trained colorization model as a decoder to expand the encoding space. This way, only distinctive color information needs to be encoded. Our experimental results demonstrate that our method can generate noise-tolerant invertible grays cales without compromising visual quality. Furthermore, a comparison with feasible baselines validates the superiority of our proposed designs.
Effectively modeling the spatio-temporal interactions both internally and externally is a challenge in controlling multi-linked snake robots. This paper presents an effective method based on deep predictive coding: Sn...
详细信息
ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
Effectively modeling the spatio-temporal interactions both internally and externally is a challenge in controlling multi-linked snake robots. This paper presents an effective method based on deep predictive coding: SnakeFormer, to address the aforementioned issue. The main contributions include: 1) Deriving a variational free energy function with two innovative regularization terms through Bayesian probabilistic analysis, offering a novel perspective to simulate the interactions between agent and the environment; 2) Introducing an interaction-attention model within a Transformer structure for predicting dynamics, and collaboratively addressing path planning and obstacle avoidance tasks. 3) By incorporating serpenoid embedding and optimizing self-attention computations, the gait stability and motion efficiency are improved. Preliminary experiments and comparative analysis with baseline models fully validate the effectiveness and generalizability of the method.
Considers the use of TCO (trellis coded quantization) in an adaptive predictive coding (APC) structure at encoding rates slightly above one and two bits per sample. The authors examine the effect of long- and short-te...
详细信息
Considers the use of TCO (trellis coded quantization) in an adaptive predictive coding (APC) structure at encoding rates slightly above one and two bits per sample. The authors examine the effect of long- and short-term predictor order on the encoding performance and study the variation in performance as a function of the TCQ codebook. The simulation results indicate that SNRs (signal/noise ratio) and SEGSNRs (segmental SNRs) as high as 25 and 24 dB, respectively, are possible at rates slightly above two bits per sample using a simple four-state trellis. At higher near one bit per sample, SNRs and SEGNRs as high as 16.5 and 15.5 dB, respectively, are possible.< >
暂无评论