Content Adaptive Interpolation Research Articles

Image interpolation by direct cross downsampling

Direct downsampling (DD) based interpolation system can be used as an encoder-decoder framework for secure data transmission, while DD is often used as a data compression way and interpolation is used for decompression. In this paper, we propose a new direct cross downsampling (DCD) algorithm, which has the same function as DD. Compared to DD, the DCD has three main differences from the angle of image interpolation: 1) DCD can use the nearest neighboring pixels for image interpolation to effectively reduce the interpolation error; 2) More pixels can be interpolated in the first step in those two-step interpolation methods with the help of the DCD; 3) The DCD preserves the information of each column or each row in the original high-resolution (HR) image while DD discards the even columns and rows. On the basis of the proposed DCD algorithm, we further propose four DCD-based interpolation methods: DCD-based bilinear interpolation, DCD-based bicubic interpolation, DCD-based content-adaptive interpolation (CAI) and DCD-based edge-oriented interpolation (EOI). By comparing the proposed four DCD-based image interpolation ways with those corresponding DD-based interpolation strategies in both experimental and analytical ways, we have proved the superiority of the DCD algorithm. Furthermore, we compared the proposed DCD-based EOI with three previous excellent DD-based interpolation works and experimental results demonstrate that the proposed DCD-based EOI has the best performance on PSNR and SSIM.

Enhancing Performance of Lossy Compression on Encrypted Gray Images through Heuristic Optimization of Bitplane Allocation

Nowadays, it remains a major challenge to efficiently compress encrypted images. In this paper, we propose a novel encryption-then-compression (ETC) scheme to enhance the performance of lossy compression on encrypted gray images through heuristic optimization of bitplane allocation. Specifically, in compressing an encrypted image, we take a bitplane as a basic compression unit and formulate the lossy compression task as an optimization problem that maximizes the peak signal-to-noise ratio (PSNR) subject to a given compression ratio. We then develop a heuristic strategy of bitplane allocation to approximately solve this optimization problem, which leverages the asymmetric characteristics of different bitplanes. In particular, an encrypted image is divided into four sub-images. Among them, one sub-image is reserved, while the most significant bitplanes (MSBs) of the other sub-images are selected successively, and so are the second, third, etc., MSBs until a given compression ratio is met. As there exist clear statistical correlations within a bitplane and between adjacent bitplanes, where bitplane denotes those belonging to the first three MSBs, we further use the low-density parity-check (LDPC) code to compress these bitplanes according to the ETC framework. In reconstructing the original image, we first deploy the joint LDPC decoding, decryption, and Markov random field (MRF) exploitation to recover the chosen bitplanes belonging to the first three MSBs in a lossless way, and then apply content-adaptive interpolation to further obtain missing bitplanes and thus discarded pixels, which is symmetric to the encrypted image compression process. Experimental simulation results show that the proposed scheme achieves desirable visual quality of reconstructed images and remarkably outperforms the state-of-the-art ETC methods, which indicates the feasibility and effectiveness of the proposed scheme.

Content adaptive single image interpolation based super resolution of compressed images

Image Super resolution is used to upscale the low resolution Images. It is also known as image upscaling .This paper focuses on upscaling of compressed images based on Interpolation techniques. A content adaptive interpolation method of image upscaling has been proposed. This interpolation based scheme is useful for single image based Super-resolution (SR) methods .The presented method works on horizontal, vertical and diagonal directions of an image separately and it is adaptive to the local content of an image. This method relies only on single image and uses the content of the original image only; therefore the proposed method is more practical and realistic. The simulation results have been compared to other standard methods with the help of various performance matrices like PSNR, MSE, MSSIM etc. which indicates the preeminence of the proposed method.

Content adaptive interpolation filters based on HEVC framework

Motion compensation is the key technique to reduce temporal redundancy in video coding. Interpolation filters are adopted to generate the inter frame prediction for motion compensation with fractional pixel accuracy. In existing video coding standards such as H.264/AVC and HEVC, a set of predefined interpolation filters is adopted in motion compensation. However, predefined interpolation filters cannot adapt to the video content, which may compromise the coding efficiency. In this paper, a content adaptive interpolation scheme is proposed for motion compensation. In the proposed scheme, a set of adaptive interpolation filters is derived for each frame as additional interpolation filters to minimize the inter prediction difference. Rate-distortion optimization is employed to choose between the predefined interpolation filters and the derived adaptive interpolation filters to achieve the best coding performance at the low bit rates. The proposed scheme is implemented into the HM 12.1 software. Experimental results show that the proposed scheme achieves 5.13 percent, 3.42 percent and 4.07 percent bit rate saving on average compared with HEVC under the “low delay P”, the “low delay B” and the “random access” configurations respectively.

Performing scalable lossy compression on pixel encrypted images

Compression of encrypted data draws much attention in recent years due to the security concerns in a service-oriented environment such as cloud computing. We propose a scalable lossy compression scheme for images having their pixel value encrypted with a standard stream cipher. The encrypted data are simply compressed by transmitting a uniformly subsampled portion of the encrypted data and some bitplanes of another uniformly subsampled portion of the encrypted data. At the receiver side, a decoder performs content-adaptive interpolation based on the decrypted partial information, where the received bit plane information serves as the side information that reflects the image edge information, making the image reconstruction more precise. When more bit planes are transmitted, higher quality of the decompressed image can be achieved. The experimental results show that our proposed scheme achieves much better performance than the existing lossy compression scheme for pixel-value encrypted images and also similar performance as the state-of-the-art lossy compression for pixel permutation-based encrypted images. In addition, our proposed scheme has the following advantages: at the decoder side, no computationally intensive iteration and no additional public orthogonal matrix are needed. It works well for both smooth and texture-rich images.

Fast super-resolution algorithms using one-dimensional patch-based training and directional interpolation

This study proposes fast super-resolution algorithms to up-scale an input low-resolution image into a high-resolution image. Conventional learning-based super-resolution algorithms require large memory space to store a huge amount of synthesis information, and they require significant computation because of the large number of two-dimensional matching operations. To mitigate this problem, the authors train a dictionary using one-dimensional patch-based training and K-means clustering at the learning phase, and they use one-dimensional matching and interpolation based on the trained dictionary at the synthesis phase. Such one-dimensional content-adaptive interpolation is applied separately in horizontal and vertical directions. In addition, the authors propose a hybrid algorithm in which directional interpolation is utilised for vertical interpolation to further reduce the dictionary size and the so called staircase artefact. Simulation results show that the proposed algorithm has higher peak-to-peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) values while providing significantly smaller dictionary size and faster computation than the latest learning-based super-resolution algorithm.

Novel Intra Deinterlacing Algorithm Using Content Adaptive Interpolation

This paper presents a novel intra deinterlacing algorithm (NID) based on content adaptive interpolation. The NID consists of three steps: pre-processing, content classification, and content adaptive interpolation. There are also three main interpolation methods in our proposed NID, i.e. modified edge-based line averaging (M-ELA), gradient directed interpolation (GDI), and window matching method (WMM). Each proposed method shows different performances according to spatial local features. Therefore, we analyze the local region feature using the gradient detection and classify each missing pixel into four categories. And then, based on the classification result, a different de-interlacing algorithm is activated in order to obtain the best performance. Experimental results demonstrate that the NID method performs better than previous techniques.