A Progressive Universal Noiseless Coder

The authors combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh's (1987) universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, they benefit from the “successive approximation” capabilities of TSVQ, thereby allowing progressive transmission of images, while retaining the ability to noiselessly encode images of unknown statistics in a provably asymptotically optimal fashion. Noiseless compression results are comparable to Ziv-Lempel and arithmetic coding for both images and finely quantized Gaussian sources

Activity detection in conversational sign language video for mobile telecommunication

The goal of the MobileASL project is to increase accessibility by making the mobile telecommunications network available to the signing Deaf community. Video cell phones enable Deaf users to communicate in their native language, American Sign Language (ASL). However, encoding and transmission of real-time video over cell phones is a powerintensive task that can quickly drain the battery. By recognizing activity in the conversational video, we can drop the frame rate during less important segments without significantly harming intelligibility, thus reducing the computational burden. This recognition must take place from video in real-time on a cell phone processor, on users that wear no special clothing. In this work, we quantify the power savings from droppin

A Progressive Universal Noiseless Coder

We describe an adaptation of Itoh and Kawabata's universal noiseless coder that allows for progressive transmission of images. The system is based on a tree structure, and codewords stored at internal nodes of the tree allow for early reproductions of the input image. When the encoder reaches a leaf of the tree, it continues transmitting until the compression is lossless. Compression results compare favorably to Ziv-Lempel coding of both images and finely quantized Gaussian sources

Generalized Multiple Description Coding through Unequal Forward Error Correction

In this paper we present an approach that uses unequal forward error correction to find one solution the generalized Multiple Description problem. Forward error correction in the Priority Encoding Transmission [1] framework achieves graceful degradation of image quality as the number of received descriptions decreases. We present the results of using our unequal forward error correction assignment algorithm [2] in the context of the Multiple Description problem and then discuss the characteristics that make it particularly effective. In generalized Multiple Description (MD) coding [3], N descriptions of a source are transmitted to a receiver, and possibly less than N are received. For transmitting over the Internet, each of the N descriptions would completely fill one of the N packets. The goal is to maximize the quality of the reconstruction given a set of received packets and the descriptions they contain. Other related papers on the MD problem include [4, 5, 6]. We use systematic ..