Papers by Keyword: Image Coding

Abstract: In this paper, an error concealment scheme for neural-network based compression of depth image in 3D videos is proposed. In the neural-network based compression, each depth image is represented by one or more neural networks. The advantage of neural-network based compression lies in the parallel processing ability of multiple neurons, which can handle the massive data volume of 3D videos. The similarity of neuron weights of neighboring nodes is exploited to recover the loss neuron weights when transmitting with an error-prone communication channel. With a simulated noisy channel, the quality of compressed 3D video, which is reconstructed undergoing the noisy channel, can be recovered well by the proposed error concealment scheme.

Abstract: In telemedicine, medical images are always considered very important telemedicine diagnostic evidences. High transmission delay in a bandwidth limited network becomes an intractable problem because of its large size. It’s important to achieve a quality balance between Region of Interest (ROI) and Background Region (BR) when ROI-based image encoding is being used. In this paper, a research made on balancing method of LS-SVM based ROI/BR PSNR prediction model to optimize the ROI encoding shows it’s much better than conventional methods but with very high computational complexity. We propose a new method using extreme learning machine (ELM) with lower computational complexity to improve encoding efficiency compared to LS-SVM based model. Besides, it also achieves the same effect of balancing ROI and BR.

Abstract: In block transform based coding, coefficients scanning plays an important role in improving compression performance. A good scanning method has great potential to remove statistical redundancy, as it can apply an energy compaction coefficients vector. In this paper, a mode-dependent (MD) coefficients scanning method is introduced, with the idea of reducing the overall bit rate by adopting side information to coding mode indexes. In the experiment, we design 128 MD scanning tables according to the DCT coefficients energy distribution, and explore the relationship between the number of scanning table used and the bit saving in image coding. The results provide reference to choose scanning modes by considering the trade-off of the increasing of side information and the decrease of data redundancy.

Abstract: In this paper, we use the embedded theory and wavelet algorithm to improve image coding, and get a new optimization algorithm of track and field image, and establish the wavelet reconstruction mathematical model of image optimization. In order to verify the effectiveness and reliability of the wavelet reconstruction algorithm, this paper uses computer embedded PLC control system and 317-2 PN/DP special CPU to establish the optimization system of track and field path, and debug a program by using Step7 software, which realizes the calculation function of embedded system. Finally, this paper does wavelet reconstruction on real time image, we obtain the reconstruction wavelet optimal path, and output it in the form of digital image, and use the drawing function to realize re-draw function of image, finally we get the route optimization figure of track and field teaching. It provides a new method for computer teaching process.

Abstract: Image coding and compression is one of the most key techniques in the area of image signal processing, However, most of the existing coding methods such as JPEG, employ the similar hybrid architecture to compress images and videos. After many years of development, it is difficult to further improve the coding performance. In addition, most of the existing image compression algorithms are designed to minimize difference between the original and decompressed images based on pixel wise distortion metrics, such as MSE, PSNR which do not consider the HVS features and is not able to guarantee good perceptual quality of reconstructed images, especially at low bit-rate scenarios. In this paper, we propose a novel scheme for low bit-rate image compression. Firstly, the original image is quantized to a binary image based on heat transfer theory. Secondly, the bit sequence of the binary image is divided into several sub-sets and each one is designated a priority based on the rate-distortion principle. Thirdly, the sub-sets with high priorities are selected based on the given bit-rate. Finally, the context-based binary arithmetic coding is employed to encode the sub-sets selected to produce the final compressed stream. At decoder, the image is decoded and reconstructed based on anisotropic diffusion. Experiments are conducted and provide convincing results.