JPEG decoding using end of block markers to concurrently partition channels on a GPU. Patrick Chieppe (u ) Supervisor: Dr.

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1 JPEG decoding using end of block markers to concurrently partition channels on a GPU Patrick Chieppe (u ) Supervisor: Dr. Eric McCreath

2 JPEG Lossy compression Widespread image format Introduction & Motivation Good compression ratio for the quality Typically decoded in the CPU Parallelizable elements in the algorithm GPU Rapidly improving technology Cost consistently decreasing over recent years Extremely efficient at parallel tasks

3 The JPEG format Four main steps in encoding Input image is divided into blocks of 8x8 pixels DCT is applied The resulting matrix is quantized and flattened in a zig-zag pattern Blocks are concatenated and compressed with Huffman encoding The decoding process is the inverse of each operation, in reverse order Notable characteristics De-quantization and IDCT can be applied to each block in parallel Huffman decoding is difficult to parallelize due to variable length codewords The quantized matrix often ends in a string of 0s, and a special end-of-block marker is encoded instead

4 Approach Exploit the presence of end-of-block markers to find block boundaries Potential locations of markers can be found by scanning for the encoded codeword s bit pattern Find the length of the block immediately following each marker Combine block lengths using O(log n) algorithm to eliminate false markers Decode, de-quantize and apply IDCT to each block in parallel

5 Block Indexing Algorithm A 1..k : k arrays of size 8n each (one element per bit position in the input bytes) A i [b] contains the combined length of the 2 i blocks following b, or -1 if no 2 i blocks start at b Iterate for each i in 1 to k-1 (sequentially): For each bit position b in 0.. 8n (in parallel): If or b + A i b = 8n A i+1 b A i b If b + A i b < 8n and A i b + A i b A i+1 b A i b + A i b + A i b Else 1 A i+1 b 1 Then, iterate in reverse to fill an array of block indexes

6 Block Length Algorithm The length of each block is needed, which can be found in a similar way. A 1..k : k arrays of size 8n each (one element per bit position in the input bytes) A i [b] contains the combined length of the 2 i codewords following b A 1 is filled with the Huffman decoding at every bit position (can be done in parallel on the GPU) Iterate for each i in 1 to k-1 (sequentially): For each bit position b in 0.. 8n (in parallel): If b + A i b < 8n and A i b + A i b A i+1 b A i b is a marker If b + A i b < 8n and A i b + A i b is not a marker or -1 A i+1 b A i b + A i b + A i b Else A i+1 b 1

7 Challenges Not all blocks will be terminated by a marker The block indexing algorithm requires all markers to be present Amount of missing blocks appears to vary depending on compression quality and level of detail All markers being present is not uncommon, but more data is needed The encoded Huffman data is byte-stuffed Requires heavy changes to the algorithms or an O(n) pass to remove stuffed bytes Efficient decoding of Huffman codes is difficult GPU memory latency is large on non-sequential data access Lookup table too big to fit in GPU shared memory, which doesn t have the same problem multiple passes are required Multiple Huffman tables The first coefficient in each block (DC) is encoded with a different table than the rest (AC) Multiple interleaved components with different tables Each coefficient can have different DC and AC Huffman tables The most common scenario is a total of 4 tables

8 Performance Evaluation All tests run on very large JPEGs at 75% compression (21600x21600, MB) Benchmark (CPU libjpeg decoding) 1.7 seconds total Marker scan 0.02 seconds (only a few bit shifts and masks) Parallel Huffman decoding 0.06 seconds Block length (finished but buggy, includes 2 passes of Huffman decoding) 0.38 seconds Block indexing (not implemented) Guessing seconds Byte un-stuffing and data copying adds second CPU time

9 Conclusions & Future Work Results so far are promising GPU times scale well but impose overhead in extra CPU time to prepare and copy the data Need more data on whether performance degrades on smaller images Need more data on whether the whole process can be offloaded to the GPU or if the size requirements are too large Implementation is difficult Need to find a solution to handle missing markers Need to handle interleaved codes from at least 4 Huffman tables to cover the most common use case Ample room for optimization of GPU code Limited GPU programming experience Need to evaluate using different kinds of memory (global vs constant vs texture vs shared memory) Possible approaches only partially explored Attempt to decode a block from each marker in parallel, discarding failed blocks

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