The letter proposes an efficient context-adaptive lossless compression method for encoding event frame sequences. A first contribution proposes the use of a deep-ternary-tree of the current pixel position context as the context-tree model selector. The arithmetic codec encodes each trinary symbol using the probability distribution of the associated context-tree-leaf model. Another contribution proposes a novel context design based on several frames, where the context order controls the codec's complexity. Another contribution proposes a model search procedure to replace the context-tree prune-and-encode strategy by searching for the closest “mature” context model between lower-order context-tree models. The experimental evaluation shows that the proposed method provides an improved coding performance of 34.34% and a smaller runtime of up to$5.18\times$compared with state-of-the-art lossless image codec FLIF and, respectively, 6.95% and$14.42\times$compared with our prior work.

The letter introduces an efficient context-based lossless image codec for encoding event camera frames. The asynchronous events acquired by an event camera in a spatio-temporal neighborhood are collected by an Event Frame (EF). A more efficient way to store the event data is introduced, where up to eight EFs are represented as a pair of an event map image (EMI), containing the spatial information, and a vector, containing the polarity information. The proposed codec encodes the EMI using:(i)a binary map, which signals the positions where at least one event occurs in the EFs;(ii)the number of events for each signalled position; and(iii)their EF index. Template context modelling and adaptive Markov modelling are employed to encode these three types of data. The experimental evaluation for EFs generated at different time intervals demonstrates that the proposed method achieves an improved performance compared with both video coding standards, HEVC and VVC, and state-of-the-art lossless image coding methods, CALIC and FLIF. When all events are collected by EFs, a relative compression of up to 5.8 is achieved compared with the raw event encapsulation provided by the event camera.

The paper proposes a novel frame-wise filtering method based on Convolutional Neural Networks (CNNs) for enhancing the quality of HEVC decoded videos. A novel deep neural network architecture is proposed for post-filtering the entire intra-coded videos. A novel scheme utilizing frame-size patches is employed for training the network. The proposed method filters the luminance channel separately from the pair of chrominance channels. A novel patch generation paradigm is proposed where, for each color channel, the corresponding mode map is generated based on the HEVC intra-prediction mode index and block segmentation. The proposed CNN-based filtering method is an alternative to the traditional HEVC built-in in-loop filtering module for intra-coded frames. Experimental results on standard test sequences show that the proposed method outperforms the HEVC standard with average BD-rate savings of 11.1% and an average BD-PSNR improvement of 0.602 dB. The average relative improvement in ΔPSNR is around 105% at QP = 42 and around 85% at QP = 32 compared with state-of-the-art machine-learning-based methods.

In recent years, a new research strategy for coding has emerged by exploring the advances brought by modern machine learning techniques. Novel hybrid coding solutions were proposed by replacing specific modules in conventional coding frameworks with more efficient modules based on innovative deep-learning (DL) methods. The paper studies first our recently proposed DL-based prediction methods for lossless image coding by analyzing their designs and employed ML concepts. A novel neural network architecture is proposed, based on a new structure of layers which proves to deliver an improved prediction compared to the reference designs. Context-tree based bit-plane coding is employed to encode the resulting prediction error. The experimental results reveal that the proposed codec reduces the lossless coding rate with 1.9% compared to state-of-the-art DL-based methods while having 4.95% less parameters. The performance gap of almost 50% compared to traditional codecs recommends the use of ML-based tools in the design of future standards for lossless image compression.

The paper proposes a novel low-complexity Convolutional Neural Network (CNN) architecture for block-wise angular intra-prediction in lossless video coding. The proposed CNN architecture is designed based on an efficient patch processing layer structure. The proposed CNN-based prediction method is employed to process an input patch containing the causal neighborhood of the current block in order to directly generate the predicted block. The trained models are integrated in the HEVC video coding standard to perform CNN-based angular intra-prediction and to compete with the conventional HEVC prediction. The proposed CNN architecture contains a reduced number of parameters equivalent to only 37% of that of the state-of-the-art reference CNN architecture. Experimental results show that the inference runtime is also reduced by around 5.5% compared to that of the reference method. At the same time, the proposed coding systems yield 83% to 91% of the compression performance of the reference method. The results demonstrate the potential of structural and complexity optimizations in CNN-based intra-prediction for lossless HEVC.

The paper proposes a novel block-wise prediction paradigm based on Convolutional Neural Networks (CNNs) for lossless video coding. A deep neural network model which follows a multi-resolution design is employed for block-wise prediction. Several contributions are proposed to improve neural network training. A first contribution proposes a novel loss function formulation for an efficient network training based on a new approach for patch selection. Another contribution consists in replacing all HEVC-based angular intra-prediction modes with a CNN-based intra-prediction method, where each angular prediction mode is complemented by a CNN-based prediction mode using a specifically trained model. Another contribution consists in an efficient adaptation of the CNN-based intra-prediction residual for lossless video coding. Experimental results on standard test sequences show that the proposed coding system outperforms the HEVC standard with an average bitrate improvement of around 5%. To our knowledge, the paper is the first to replace all the traditional HEVC-based angular intra-prediction modes with an intra-prediction method based on modern Machine Learning techniques for lossless video coding applications.

This paper proposes a novel approach for lossless image compression. The proposed coding approach employs a deep-learning-based method to compute the prediction for each pixel, and a context-tree-based bit-plane codec to encode the prediction errors. First, a novel deep learning-based predictor is proposed to estimate the residuals produced by traditional prediction methods. It is shown that the use of a deep-learning paradigm substantially boosts the prediction accuracy compared with the traditional prediction methods. Second, the prediction error is modeled by a context modeling method and encoded using a novel context-tree-based bit-plane codec. Codec profiles performing either one or two coding passes are proposed, trading off complexity for compression performance. The experimental evaluation is carried out on three different types of data: photographic images, lenslet images, and video sequences. The experimental results show that the proposed lossless coding approach systematically and substantially outperforms the state-of-the-art methods for each type of data.

This work proposes the fi rst block -wise prediction paradigm based on CNNs for lossless video coding. The propose prediction scheme improves the HEVC performance by replacing a set of 9 angular intra-prediction modes with an improved CNN -based prediction; these include m2, m6, m10, m14, m18, m22, m26, m30, m34. A causal neighborhood selecting a 16 x 16 block around the currently predicted block is used as input. The novel neural network model called Angular intra-Prediction Convolutional Neural Network (AP -CNN) is designed based on the U -net architecture and operates on three resolutions (16 x 16, 8 x 8,4 x 4). AP -CNN contains the following U-net structure: 10 convolutional layers (2 + 2, 2 + 2, 2) with (32, 64, 128) fi lters; 2 deconvolution layers with 32 and 64 fi lters; 2 filter concatenation layers. A final convolutional layer with one filter is used to compute a 16 x 16 block, which is further clipped out on the bottom-right corner to obtain the 4 x 4 output predicted block. AP-CNN uses a 3 x 3 window and ReLU and it employs the Adam optimizer with MSE loss function. The experimental assessment is carried out on the Y channel of two datasets: 15 HEVC Test Sequences on 8 -bit, and 7 TUT Sequences from Ultra Video Group (TUT-vSEQ). A model is trained for each of the 9 modes using a corresponding training set generated based on HEVC's optimal mode segmentation applied to 15 HD sequences from Xiph.org and a collection of RGB images from NYC Library. The size of each of the training sets varies between 6700 and 37300 batches, where one batch contains 500 samples (input blocks). Each AP -CNN model was trained during 20 epochs, and using a 90% -10% ratio for training -validation data splitting. Table 1 shows compression results for HEVC running under the lossless setting and with all intra-prediction, and for AP-CNN where Lossless HEVCIntra employs the CNN-based predictors. AP-CNN outperforms Lossless HEVC with an average bitrate improvement of around 0.85%. An increased performance is obtained on 1080p resolutions and above. The improved coding performance is due to AP-CNN's capability to combine linear and nonlinear CNN-based prediction models.

In this paper, a novel approach to encode lenslet (LL) images is proposed. The method departs from traditional block-based coding structures and employs a hexagonal-shaped pixel cluster, called macro-pixel, as an elementary coding unit. A novel prediction mode based on dictionary learning is proposed, whereby macro-pixels are represented by a sparse linear combination of atoms from a generic dictionary. Additionally, an optimized linear prediction mode and a directional prediction mode specifically designed for macro-pixels are proposed. Rate-distortion optimization is utilized to select the best intra prediction mode for each macro-pixel. Experimental results on the light field image data set show that the proposed coding system outperforms HEVC and the state-of-the-art in LL image coding with an average peak signal to noise ratio gain of 3.33 and 1.41 dB, respectively, and with rate savings of 67.13% and 34.30%, respectively.

The paper introduces a novel macro-pixel prediction method based on Convolutional Neural Networks (CNN) for lossless compression of light field images. In the proposed method, each macro-pixel is predicted based on a volume of macro-pixels from its immediate causal neighborhood. The proposed deep neural network operates on these macro-pixel volumes and provides accurate macro-pixel prediction in light field images. The resulting macro-pixel residuals are encoded by a reference codec built based on the CALIC codec. A context modeling method for light field images is proposed. Experimental results on a large light field image dataset show that the proposed prediction method systematically and substantially outperforms state-of-the-art predictors. To our knowledge, the paper is the first to introduce deep-learning based prediction of macro-pixels, enabling efficient lossless compression of light field images.

The paper proposes a novel prediction paradigm in image coding based on Convolutional Neural Networks (CNN). A deep neural network is designed to provide accurate pixel-wise prediction based on a causal neighbourhood. The proposed CNN prediction method is trained on the high-activity areas in the image and it is incorporated in a lossless compression system for high-resolution photographic images. The system uses the proposed CNN-based prediction paradigm as well as LOCO-I, whereby the predictor selection is performed using a local entropy-based descriptor. The prediction errors are encoded using a CALIC-based reference codec. The experimental results show a good performance for the proposed prediction scheme compared to state-of-the-art predictors. To our knowledge, the paper is the first to introduce CNN-based prediction in image coding, and demonstrates the potential offered by machine learning methods in coding applications.

The paper deals with encoding the contours of given regions in an image. All contours are represented as a sequence of contour segments, each such segment being defined by an anchor (starting) point and a string of contour edges, equivalent to a string of chain-code symbols. We propose efficient ways for anchor points selection and contour segments generation by analyzing contour crossing points and imposing rules that help in minimizing the number of anchor points and in obtaining chain-code contour sequences with skewed symbol distribution. When possible, part of the anchor points are efficiently encoded relative to the currently available contour segments at the decoder. The remaining anchor points are represented as ones in a sparse binary matrix. Context tree coding is used for all entities to be encoded. The results for depth map compression are similar (in lossless case) or better (in lossy case) than the existing results.

We introduce a method to create lossy versions of one image, either by successively merging the constant regions of the original image, or by iteratively splitting the regions from a created lossy image using horizontal or vertical line segments. Merging and split decisions are greedily taken, according to the best slope towards next point in the rate-distortion curve. For each created lossy image, the region contours and the optimal depth values can be entropy coded in three ways: with a new algorithm, or with two existing lossless coding algorithms. The obtained results compare favorably with the existing lossy methods.

This paper introduces an efficient method for lossless compression of depth map images, using the representation of a depth image in terms of three entities: 1) the crack-edges; 2) the constant depth regions enclosed by them; and 3) the depth value over each region. The starting representation is identical with that used in a very efficient coder for palette images, the piecewise-constant image model coding, but the techniques used for coding the elements of the representation are more advanced and especially suitable for the type of redundancy present in depth images. Initially, the vertical and horizontal crack-edges separating the constant depth regions are transmitted by 2D context coding using optimally pruned context trees. Both the encoder and decoder can reconstruct the regions of constant depth from the transmitted crack-edge image. The depth value in a given region is encoded using the depth values of the neighboring regions already encoded, exploiting the natural smoothness of the depth variation, and the mutual exclusiveness of the values in neighboring regions. The encoding method is suitable for lossless compression of depth images, obtaining compression of about 10-65 times, and additionally can be used as the entropy coding stage for lossy depth compression.