Multiple Description Wavelet-Based Image Coding Using Iterated Function System

Recent literature highlights the multiple description coding (MDC) as a promising method to solve the problem of resilient image coding over error-prone networks, where packet losses occur. In this paper, we introduce a novel multiple description wavelet-based image coding scheme using fractal. This scheme exploits the fractal’s ability, which is to describe the different resolution scale similarity (redundancy) among wavelet coefficient blocks. When one description is lost, the lost information can be reconstructed by the proposed iterated function system (IFS) recovering scheme with the similarity and some introduced information. Compared with the referenced methods, the experimental results suggest that the proposed scheme can achieve better performance. Furthermore, it is substantiated to be more robust for images transmission and better subjective quality in reconstructed images even with high packet loss ratios

Pengiriman Citra Dengan Teknik Multiple Description Berbasis Perangkat Lunak

Tingginya kerusakan dan kehilangan paket data pada pengiriman citra / video melalui kesalahan saluran yang rawan akibat karena penutupan saluran sementara dan adanya noise yang berakibat banyak terjadinya paket data yang hilang yang mengakibatkan kwalitas suatu citra menjadi rusak atau gambar yang dikirim banyak noisenya. Untuk itu dibutuhkan suatu sistem yang dengan memanfaatkan sebuah tekhnologi yang sedang berkembang saat ini.Pengkodean Multi Deskripsi (PMD) atau disebut dengan Multiple Description Coding (MDC) adalah teknik pengkodean sumber yang memisahkan deskripsi dari Pengkodean Deskripsi Tunggal (PDT) menjadi multi deskripsi. PMD mengatasi permasalahan transmisi pada PDT yang menggunakan protokol TCP ketika ada paket data PDT yang hilang.Pengkodean Multi Deskripsi (PMD) menggunakan Deskripsi Multiple Skalar Quantizer (MDSQ) untuk menghasilkan dua deskripsi dari suatu gambar yang akan dikirimkan dengan menggunakan lebih dari dua saluran transmisi. Jika hanya satu saluran transmisi yang bekerja dengan dengan baik dan optimal, maka informasi yang diterima PMD Decoding dari pengirim, maka hasil citra yang akan diproses dan diterima hasil yang didapatkan cukup untuk mencapai hasil minimum. Apabila jika kedua saluran berfungsi atau bekerja dengan baik dan optimal serta maksimal tanpa ada kendala dan masalah pada saluran transmisi, maka informasi yang diterima akan lebih maksimal. Pada proses PMD Decoding sendiri akan menggabungkan informasi dari kedua saluran tersebut untuk mencapai hasil kualitas citra yang lebih tinggi. Untuk PMD sendiri dengan prinsip Deskripsi Multiple Skalar Quantizer, maka masing-masing deskripsi informasi yang diterima akan menggabungkan dari kedua informasi pengiriman sehingga estimasi deskripsi yang hilang dapat dilakukan berdasarkan deskripsi yang diterima. Sehingga hasil yang didapatkan cukup untuk hasil yang diinginkan. Kata Kunci : Kehilangan Data,Kanal AWGN,Transformasi kuantisasi,PMD(Pengkodean Multi Description), SDC (Pengkodean Deskripsi Tunggal

An Efficient MDC based Set Partitioned Embedded Block Image Coding

In this paper, fast, efficient, simple and widely used Set Partitioned Embedded bloCK based coding is done onMultiple Descriptions of transformed image. The maximum potential of this type of coding can be exploited with discretewavelet transform (DWT) of images. Two correlated descriptions are generated from a wavelet transformed imageto ensure meaningful transmission of the image over noise prone wireless channels. These correlated descriptions areencoded by set partitioning technique through SPECK coders and transmitted over wireless channels. Quality ofreconstructed image at the decoder side depends upon the number of descriptions received. More the number ofdescriptions received at output side, more enhance the quality of reconstructed image. However, if any of the multipledescription is lost, the receive can estimate it exploiting the correlation between the descriptions. The simulationsperformed on an image on MATLAB gives decent performance and results even after half of the descriptions islost in transmissio

Multiple Description Quantization via Gram-Schmidt Orthogonalization

The multiple description (MD) problem has received considerable attention as a model of information transmission over unreliable channels. A general framework for designing efficient multiple description quantization schemes is proposed in this paper. We provide a systematic treatment of the El Gamal-Cover (EGC) achievable MD rate-distortion region, and show that any point in the EGC region can be achieved via a successive quantization scheme along with quantization splitting. For the quadratic Gaussian case, the proposed scheme has an intrinsic connection with the Gram-Schmidt orthogonalization, which implies that the whole Gaussian MD rate-distortion region is achievable with a sequential dithered lattice-based quantization scheme as the dimension of the (optimal) lattice quantizers becomes large. Moreover, this scheme is shown to be universal for all i.i.d. smooth sources with performance no worse than that for an i.i.d. Gaussian source with the same variance and asymptotically optimal at high resolution. A class of low-complexity MD scalar quantizers in the proposed general framework also is constructed and is illustrated geometrically; the performance is analyzed in the high resolution regime, which exhibits a noticeable improvement over the existing MD scalar quantization schemes.Comment: 48 pages; submitted to IEEE Transactions on Information Theor

Patch-Based Holographic Image Sensing

Holographic representations of data enable distributed storage with progressive refinement when the stored packets of data are made available in any arbitrary order. In this paper, we propose and test patch-based transform coding holographic sensing of image data. Our proposal is optimized for progressive recovery under random order of retrieval of the stored data. The coding of the image patches relies on the design of distributed projections ensuring best image recovery, in terms of the $\ell_2$ norm, at each retrieval stage. The performance depends only on the number of data packets that has been retrieved thus far. Several possible options to enhance the quality of the recovery while changing the size and number of data packets are discussed and tested. This leads us to examine several interesting bit-allocation and rate-distortion trade offs, highlighted for a set of natural images with ensemble estimated statistical properties