Image interpolation using Shearlet based iterative refinement

This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, and (c) extracting high frequency information from the approximation to refine the initial estimate. For the sparse modeling, a shearlet dictionary is chosen to yield a multiscale directional representation. The proposed algorithm is compared to several state-of-the-art methods to assess its objective as well as subjective performance. Compared to the cubic spline interpolation method, an average PSNR gain of around 0.8 dB is observed over a dataset of 200 images

Decoder Hardware Architecture for HEVC

This chapter provides an overview of the design challenges faced in the implementation of hardware HEVC decoders. These challenges can be attributed to the larger and diverse coding block sizes and transform sizes, the larger interpolation filter for motion compensation, the increased number of steps in intra prediction and the introduction of a new in-loop filter. Several solutions to address these implementation challenges are discussed. As a reference, results for an HEVC decoder test chip are also presented.Texas Instruments Incorporate

A layered approach to the integration of lossy and lossless image compression

We propose a wavelet-based image coding scheme allowing lossless and lossy compression, simultaneously. Our two-layered approach picks up the best of two worlds: it uses a high-performing wavelet-based coding technique for lossy compression in the low bit range as a first stage. For the second (optional) stage we extend the concept of reversible integer wavelet transforms to the more flexible class of integer wavelet packet transforms which allows the generation of a whole library of bases from which a best representation for a given residual between the reconstructed lossy compressed image and the original image is chosen using a fast search algorithm. We present experimental results revealing that our compression algorithm yields a rate-distortion performance similar or superior to the best currently published pure lossy still image coding methods. At the same time, the lossless compression performance of our two-layered scheme matches those of state-of-the-art pure lossless image coding schemes. Compared to other proposed lossy/lossless coding algorithms our method achieves a considerable gain in PSNR of 0.4-2.5 db for lossy reconstructions of typical images at typical rates, while simultaneously the corresponding lossless compression ratios are quite competitive

Network-optimized adaptive SVC-based live video streaming

In this work, a network-optimized live scalable video streaming scheme with adaptive preprocessing is presented, while showing significant improvements in visual quality. According to the proposed scheme, an adaptive pre-filter is applied prior to encoding each Scalable Video Coding (SVC) layer, while each pre-filter parameters are dynamically adjusted according to varying network conditions, such as the network Round Trip Time (RTT) and packet loss ratio, thereby enabling to continuously obtain an optimal visual quality at the decoder side. The performance of the presented scheme is evaluated and tested in detail, thereby demonstrating a significant gain of up to 5dB