Neural Representations for Videos (NeRV) is a neural network model that learns video representation through image-wise implicit neural representation (INR). Although early NeRV-based research has improved representation learning, it overlooked video properties. In this paper, we propose a new method called Scene-Adaptive Neural Representation for Videos (SANeRV) that considers videos’ general properties and adapts to individual video characteristics. To achieve this, we incorporate temporal redundancy, which is a typical feature of videos, by splitting the learning process into two parts: global representation and residual representation. Moreover, we adjust the network architecture based on the presence of scene changes and the dynamism of the video to cater to individual video characteristics. Experiments show that SANeRV achieves state-of-the-art performance in video regression and compression tasks for benchmark datasets.

This paper presents a lossless color filter array (CFA) image compression method that attempts to maximize the use of image correlations from two perspectives. First, we adopt a hierarchical approach for decomposing an input into subimages and proceed with the subimage encoding with a new encoding order. The new encoding scheme is shown to provide better prediction performances compared to conventional raster scan orders. Secondly, for each subimage, we compress the low-frequency components first and use them as additional input for encoding the remaining high-frequency components. In this scenario, the high-frequency components show improved compression efficiency since they use the low-frequency ones as strong prior. Experiments show that the proposed method achieves state-of-the-art performance for 8∼16 bits raw images.

Recent learning-based lossless image compression methods encode an image in the unit of subimages and achieve comparable performances to conventional non-learning algorithms. However, these methods do not consider the performance drop in the high-frequency region, giving equal consideration to the low and high-frequency areas. In this paper, we propose a new lossless image compression method that proceeds the encoding in a coarse-to-fine manner to separate and process low and high-frequency regions differently. We initially compress the low-frequency components and then use them as additional input for encoding the remaining high-frequency region. The low-frequency components act as a strong prior in this case, which leads to improved estimation in the high-frequency area. In addition, we design the frequency decomposition process to be adptive to color channel, spatial location, and image characteristics. As a result, our method derives an image-specific optimal ratio of low/high-frequency components. Experiments show that the proposed method achieves state-of-the-art performance for benchmark high-resolution datasets.

This paper presents a channel-wise progressive coding system for lossless compression of color images. We follow the classical lossless compression scheme of LOCO-I and CALIC, where pixel values and coding contexts are predicted and forwarded to the entropy coder for compression. The contribution is that we jointly estimate the pixel values and coding contexts from neighboring pixels by training a simple multilayer perceptron in a residual and channel-wise progressive manner. Specifically, we obtain accurate pixel prediction along with coding contexts that reflect the magnitude of local activity very well. These results are sent to an adaptive arithmetic coder that appropriately encodes the prediction error according to the corresponding coding context. Experimental results demonstrate the effectiveness of the proposed method in high-resolution datasets.