Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding - 2018 PROJECT TITLE :Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding - 2018ABSTRACT:Transform-domain downward conversion (TDDC) for image coding is usually implemented by discarding some high frequency components from every reworked block. Therefore, a block of fewer coefficients is created, and a lower compression cost is achieved due to the coding of solely some low-frequency coefficients. During this Project, we specialize in the look of a replacement TDDC-based coding technique by using our proposed interpolation compression directed filtering (ICDF) and error-compensated scalar quantization (ECSQ), resulting in the compression dependent TDDC (CDTDDC)-based mostly coding. More specifically, ICDF is initial used to convert each 16 × sixteen macro-block into an eight × eight coefficient block. Then, this coefficient block is compressed with ECSQ, ensuing in a smaller compression distortion for those pixels that locate at some specific positions of a macroblock. We have a tendency to choose these positions in keeping with the four:one uniform sub-sampling lattice and use the pixels locating at them to reconstruct the full macro-block through an interpolation. The proposed CDTDDC-based mostly coding can be applied to compress both grayscale and color images. A lot of importantly, when it's utilized in the color image compression, it offers not only a replacement answer to scale back the info-size of chrominance elements but additionally the next compression potency. Experimental results demonstrate that applying our proposed CDTDDC-based mostly coding to compress still pictures can achieve a vital quality gain over the present compression methods. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Color Balance and Fusion for Underwater Image Enhancement - 2018 Co-Saliency Detection for RGBD Images Based on Multi-Constraint Feature Matching and Cross Label Propagation - 2018